AutoRAG Evaluation Datasets
Collection
4 items • Updated • 1
doc_id stringlengths 36 36 | contents stringlengths 22 3.25k | metadata dict |
|---|---|---|
6f86094c-47fe-43de-a77a-e8c34c69c997 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
Jianhao Yuan1, Shuyang Sun1, Daniel Omeiza1, Bo Zhao2, Paul Newman1, Lars Kunze1, Matthew Gadd1
1 University of Oxford 2 Beijing Academy of Artificial Intelligence
{jianhaoyuan,kevinsun,dan... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
cf485ad0-8ec4-4a63-a0c6-5d7eb499c0c8 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## I. Introduction
Driven by the emerging development of deep learning, autonomous driving has observed a paradigm shift from rulesbased decision systems [66, 21] to data-driven learning-ba... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
f60a5f7d-00dd-47bd-9b9f-55b76d1a571f | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## I. Introduction
56], coupled with the significant domain shift across various datasets [23], which hinders the models' generalisation capacity to novel environments outside of the traini... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
ddf70265-668c-4d1e-a9eb-89c2130c0ab5 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## Ii. Related Work A. Explainable End-To-End Autonomous Driving
End-to-end learned driving [13] maps directly from raw sensor input to vehicle control signals. This data-driven, joint opti... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
b3a710c0-0739-485b-b618-5023b4a09318 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## B. Multi-Modal Large Language Model
Recent advancements in Large Language Models (LLMs)
have paved the way for the emergence of Multi-modal Large Language Models (MLLMs) [1, 70]. Benefit... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
8cdc469e-cad8-41db-bbe6-621e78abf3fa | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## B. Multi-Modal Large Language Model
external perception module feedback [53, 24, 14], designed chain-of-thought reasoning template [53, 52] or downstream planner [67, 69, 14] to form a ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4a55f9ba-307c-41d7-9ee8-6da90b9b277f | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. In-Context Learning And Retrieval-Augmented Generation
While LLMs demonstrate strong generative and reasoning capacity, there are still several issues associated with their output, su... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
9a8dbd67-a0e4-409f-91d1-128f0c1222d9 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## Iii. Method
RAG-Driver is a retrieval-augmented, multi-modal large language model (MLLM) for generalisable explainable end-to-end driving. Its multi-tasking capabilities encompass three ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
f5206e64-9ffa-466b-aff8-8221e5051355 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## A. Multi-Modal Large Language Model Architecture
Following the successful MLLM paradigm of Video-
LLaVA [46], we align visual and language embedding through visual instruction tuning. We... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
f2ec5109-229c-485d-8689-16d49f6b6591 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## A. Multi-Modal Large Language Model Architecture
26] as an activation function. We train the projector with a two-stage training strategy as detailed in Sec. III-B.
Large Language Mode... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
743d0fc1-7548-41e6-b05a-ba88868f428e | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## B. Training Strategy
Following the visual instruction tuning paradigm [48, 46], we employ a two-stage training strategy to progressively enable cross-modality alignment and multi-task dr... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
61657740-f0b8-46a0-9710-91caf606b357 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Retrival-Augmented In-Context Learning
Another critical component of the system is the memory unit, which consists of a database and a retrieval engine. The database incorporates vect... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
1a02fa44-6d3c-42b8-b10a-70ce8d63ca40 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Retrival-Augmented In-Context Learning
retrieval through an efficient vector similarity search. Given a query vector sq the cosine similarity between the query vector and each vector... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
b7bb70f6-6ff5-49ba-b881-931414658e73 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Retrival-Augmented In-Context Learning
an estimated meta-gradient. We provide in our work a new, alternative derivation of this, supported by more recent work in [40] on the followin... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
e906ded7-684a-4f17-b300-11ad548a6ad2 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Retrival-Augmented In-Context Learning
\sum_{i}\eta\frac{\partial L}{\partial{\bf y}}|_{{\bf y}_{i}}{\bf x}_{i}^{T}{\bf x}\tag{11}$$
where xi, yi are the (mini-batch) input and output... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c477fdd0-bd61-4aac-99c7-7a435e3700a5 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## Iv. Experiments A. Settings And Datasets
We empirically evaluate the proposed Retrieval-augmented In-Context Learning (RA-ICL) framework within the Multimodal Large Language Model (MLLM)... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
ff933a31-24e9-4308-a3e5-545c19646a73 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## Iv. Experiments A. Settings And Datasets
the embedding projector, we train the model for 300 epochs. Further experiment implementation details are provided in Apx. B.
Evaluation Metri... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
81f6e47b-f7f4-4d92-a24d-ef00f465029f | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## B. Explainability In Driving Action And Justification
We begin by evaluating the quality and accuracy of explanations and justifications for driving actions separately.
As shown in upp... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
df58e2f5-341b-4311-8bd9-b47a53240ba1 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Control Signal Prediction
We next evaluate the accuracy of control signal predictions for Course (i.e., turning angle) and Speed. As indicated in Tab. II, our method surpasses others ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4d1467ce-46da-4f0c-b39d-55e6a4481950 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## D. Ablation Study On Retrieval Strategy
We perform a more comprehensive ablation study to evaluate the efficacy of our proposed retrieval-augmented in-context learning. We first aim to i... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
28191f10-2bf1-40bd-ad19-8b175342fedd | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## E. Generalisation Capacity
One of the critical capacities of autonomous systems is to generalise to unseen environments out of its training distribution. However, in the domain of explai... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
e178f548-363b-4114-b936-1fd5d35a5d5f | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## F. Qualitative Demonstration
We also demonstrate a series of quantitative examples comparing the driving action explanation and justification provided by human ground truth and predictio... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
f9688efe-918a-49ec-bfab-7012b515360a | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
This work aims to develop a generalisable explainable driving commentator using a Machine Learning Language Model
(MLLM), addressing a significant obstacle... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
06b62413-e05e-4459-b34d-1b33a1ab3cf3 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| |
| M | |
| ↑ | ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
5d654151-d6e7-4518-8b01-2e39d16a3dca | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
|
| M | |
| ↑ | |
| In Distribution Per... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c3076fc6-0cc1-4f21-9cba-806c9a558e94 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| |
| 27 | . |
| SAA [ ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
3390a9ac-b081-4042-a237-147ff00b5c3f | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
|
| WAA [ | 38 |
| × | |
| 32 ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4108bdda-4f3d-4597-9c50-5cf96e2bbe79 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
|
| ✓ | |
| 30 | . |
| OURS ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
bb8552ea-715a-4e4c-bc48-5e79ebe4a0df | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
|
| 14 | . |
| + | |
| 21 ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
1d9b2328-b577-4ea8-9020-4bdbebfb5780 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| . |
| + | |
| 6 | . |
| ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c26d8270-2836-4905-9a37-f63b2a5d2ad5 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
|
| 0 | . |
| Base w/o ICL | |
| ✓ ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
6a714d88-f2d8-4c27-b3bc-24c5c66b99bf | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| . |
| ∆ | |
| w.r.t SOTA | + |
| − ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
d41f7d38-cafd-4708-a967-6f995de28cb5 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| |
| 3 | . |
| +80.6% | + ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
a4c94f02-363e-4ef3-ac77-4b2700c00054 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
A0.5 ↑
A1.0 ↑
A5.0 ↑
A10.0 ↑
ADAPT [33]
5.87
54.49
86.39
91.06
97.36
98.20
2.68
11.77
31.79
47.48
92.75
95.87
DriveGPT4 [78]
4.57
69.22
79.14
84.47
95.72
... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
2a65b580-97b7-48fd-9937-ebaa50921e62 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| 8 | 222 | . | 1 | 5 | . | 9 | 58 |
| � | � | ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
fe5c3cb7-3602-492c-9a30-926b537043fc | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| | | | | | |
| 0 | . | 0 | 0 | . | ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4d8519c0-f34c-46de-8e0d-45e48c2a9861 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## V. Limitations And Future Work
| . | 7 | 83 |
| � | � | � | | | | | ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
dc3aaf74-2424-4638-b075-ed53c2ae8a86 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## Vi. Conclusion
We propose *RAG-Driver*, a Multi-Modal Large Language Model with Retrieval-augmented In-context Learning capacity designed for generalisable and explainable end-to-end dri... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
2b5d8ed3-d19c-446b-92ee-b044dd3874bc | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## Appendix
A. Discussion and Limitation Scale of MLLM While our Multi-modal Large Language Model (MLLM) has exhibited impressive capabilities in visual reasoning and planning for driving t... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
5cb48f63-a287-4579-8ab7-1819873b8fe1 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Baseline Details
In our comparison, we evaluate several baseline methods. The first, S2VT [74], utilises an end-to-end sequence-to-sequence model with Long Short-Term Memory (LSTM) ne... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
953a7b23-907a-41a4-ae12-1191c4078062 | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Baseline Details
the layer that resulted in the weight update. Now, if we in fact have optimised over a mini-batch of input-outputs xi, yi we have
i η ∂L i η ∂L ∂y |yixT i x ∆W = �... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
a8a387f2-6d44-4733-9a2e-38038676044b | # Rag-Driver: Generalisable Driving Explanations With Retrieval-Augmented In-Context Learning In Multi-Modal Large Language Model
## C. Baseline Details
the terms both pre-multiplying WQz1:n.
Note that WZSL is independent of the in-context terms
(depending only on the query). Now, in ∆WICL we in fact
have a set of in... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10828v1.md",
"file_path": "paper_data/2402.10828v1.md",
"file_size": 64885,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
61283a25-c49b-41c9-b543-a39f62acab7d | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
Ziru Chen1, Michael White1, Raymond Mooney2, Ali Payani3, Yu Su1**, Huan Sun**1
1The Ohio State University
2The University of Texas at Austin
3Cisco Research
{chen.8336, white.1240, su.809, sun.397}@osu.edu mooney@cs.utexas.edu apayani@cisc... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
dd18c4d9-d660-4fa1-8ed1-cb7b146922f7 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Abstract
In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practical utili... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
18e72280-da5f-451d-83d7-4d8f342314ef | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 1 Introduction
Planning plays a crucial role in intelligent behaviors of human and AI agents. Since the early stage of AI research, various methods have been proposed to build agents that can plan efficiently and accurately (Newell and S... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
9a2f8070-708f-4173-b1cc-bedd49d0e159 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 1 Introduction
the performance of language agents using different planning methods? (RQ2)
Can LLM-based discriminators correctly assess language agents' actions in practical settings?
To this end, we analyze LLMs' discrimination abili... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
e54d7ca9-5b5e-4a18-b2d8-8f67d4cdf6d0 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 2 Related Work
A lot of recent research efforts have focused on advanced planning methods for improving the multistep problem-solving abilities of LLMs (Li et al. 2023b; Madaan et al. 2023; Wang et al. 2023b; Yao et al. 2023a,b; Zhou et ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
73fd7d0e-9316-4220-ba49-576854c391f0 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 3 Our Framework
As shown in Figure 1, we systematically analyze different planning methods in a unified generatordiscriminator framework. Our framework consists of a generator that proposes (partial) action sequences, a discriminator tha... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
5a16098d-b5ef-4177-b345-dc64c3ef229e | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 3.1 Generator
For each planning step, we prompt the generator to sample action sequences (SQL queries or Python
| (a) Re-ranking. | (b) Iterative Correction. | (c) Tree Search. |
|-------------------|-----------------------------... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
5b08c471-c91c-4348-9132-d2167399ca04 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 3.2 Discriminator
Given some (partial) action sequences, we formulate the discrimination task as binary question answering (Kadavath et al., 2022; Ke et al., 2023).
The discrimination score of each tested example is the probability of ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c6c15b72-25ff-4f29-8d08-0483db49751b | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 3.3 Planning Methods
Re-ranking. Re-ranking is a straightforward planning method. After sampling a few complete action sequences from the generator, it uses the discriminator to score them and return the highest-scoring plan (Figure 2a).... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
d22f6a32-254f-4924-aa1e-4c41702f66c2 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 3.3 Planning Methods
et al.,
2023a), and Language Agent Tree Search (Zhou et al., 2023). It uses a memory structure (e.g., a heap) to store observed partial action sequences and their scores. For each iteration, it prompts the generator... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
df384d3d-a220-4949-ae96-d76e274f5bb1 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 4 Experimental Setup 4.1 Tasks And Datasets
Text-to-SQL Parsing. Text-to-SQL parsing is a code generation task of mapping natural language utterances to SQL queries. It requires agents to ground utterances to database environment and gen... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
491860b5-47f4-49d9-a16b-6d4c98cf4bc0 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 4.2 Models
In all experiments, we use CodeLlama-13B- Instruct as the generator in our framework. We also evaluate three kinds of LLMs as the discriminator:
(1) *open-source LLMs*: CodeLlama-7B-Instruct and CodeLlama-13B-Instruct (Rozière... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
6947d985-a3b5-4c70-943b-2cce356fcf93 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 4.3 Evaluation
Intrinsic Evaluation. We measure the discrimination abilities of LLMs with four intrinsic metrics.
(1) Discrimination accuracy (Acc): Given a pair of correct and wrong programs, we calculate the percentage where the corr... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
2962f4a2-db19-4246-8fc3-5b534c812ca8 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 5 Simulation Experiments With Oracle 5.1 Oracle-Based Discriminator
To investigate how discrimination accuracy affects the overall performance of language agents using different planning methods (RQ1), we utilize oracle environmental fee... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
287d1329-8a0c-46b8-b93a-372fb7c300fd | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 5.2 Results And Analysis
As shown in Figure 3, discrimination accuracy closely correlates with the performance of agents on all three datasets, no matter which planning method is used. For instance, the performance of re-ranking agents i... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
6dcb7d6e-5544-47e6-b11f-94d265454506 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 6 Llm-Based Discriminators
iterative correction and tree search as follows:
Advanced planning methods demand highly accurate discriminators. For iterative correction agents, their performance usually cannot distinguish from the re-rankin... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4a2c1a24-3638-4cc1-9db6-68993ae66fde | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 6 Llm-Based Discriminators
K‡
Acc
F1
H@1
MRR
Acc
F1
H@1
MRR
Acc
F1
H@1
MRR
CodeLlama-7B
54.0
37.1
56.0
62.3
44.6
46.7
13.0
18.0
48.6
38.7
36.2
46.9
CodeLlama-13B
58.2
37.1
57.0
63.1
49.4
46.7
12.7
18.3
62.2
38.7
41.8
51.0
CodeLlama-7B-FT... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
dbb835f4-67d0-4f46-8160-c886cc4eded1 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 6 Llm-Based Discriminators
| | | | | CodeLlama-13B | GPT-3.5-Turbo | CodeLlama-13B-FT |
|-----------------------|------|-------|--------|------|-----------------|-----------------|--------------------|
... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
cc89dd28-f0f4-4a33-b140-ce0904003b39 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 6 Llm-Based Discriminators
|
| 83.6 | 79.6 | 70.6 | 90.0 | 89.2 | 76.5 | 88.5 | 85.1 |
| Improvement | 25.4 | 30.2 | 8.4 | 23.0 | 24.9 | 4.4 ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
267e601c-a834-4422-85c0-7b8bccfe55ae | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 6.1 Naive Discriminators
As Table 1 shows, most open-source LLMs have mediocre discrimination abilities. After fine-tuning, CodeLlama-13B-FT could reach the same level of performance as GPT-3.5. In comparison, closedsource LLMs exhibit s... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
2a5368ab-227e-4c36-a035-1a23f4eb2f3c | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 6.2 Observation-Enhanced Discriminators
To improve LLMs' discrimination abilities, we conduct an error analysis for CodeLlama-13B on its worst-performing intrinsic evaluation set, Bird. We sample 50 pairs of SQL queries from the Bird int... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
1762c9bd-211c-41ba-b49f-3c9a3428cfaf | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7 End-To-End Evaluation
While we have evaluated their discrimination abilities with a fixed test set, to answer *RQ2*, we wonder if LLMs can correctly assess constantly changing sets of programs in actual planning processes. To this end,... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
20d6fe4d-909e-4d01-86f3-ca999f383ba1 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.1 Text-To-Sql Parsing
As shown in Table 3, agents using naive LLM-
based discriminators do not perform well on textto-SQL parsing. On Spider, the re-ranking agent using CodeLlama-13B-FT has the best accuracy
(61.5), which is still lowe... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
98b211e0-bda6-4571-b179-1e04a2ec35b7 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.1 Text-To-Sql Parsing
.3
show an accuracy of 18.0, which is slightly higher than greedy generation (16.0). In addition to the mediocre performance, we find that when using naive discriminators, iterative correction and tree search co... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
0f4fbe8e-bade-47cd-9ac2-d0e7beccb27b | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.2 Mathematical Reasoning
The most interesting result in mathematical reasoning evaluation (Table 4) is the failure of iterative correction with naive discriminators. When prompting the generator CodeLlama-13B for 0- shot correction, it... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
ec48c217-a813-4530-b24a-726e36312bf3 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.2 Mathematical Reasoning
|
| 50.0 | |
| CodeLlama-13B | |
| E | ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
d0321866-04c0-4469-a9ef-bf3d33602569 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.2 Mathematical Reasoning
| |
| 47.6 | 48.4 |
| 51.0 | |
| Oracle Simulation | ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
61019431-fbbd-42d5-8fb7-d8563b80f43a | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.2 Mathematical Reasoning
|
| ) | |
ing GPT-3.5-Turbo as the discriminator, it is less severe because GPT would sometimes assign a high score (> 0.99) to the initial Py... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
0e6007b4-e302-4d18-a9ef-3e48b3ea5430 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.3 Analysis
To better understand the end-to-end evaluation results, we conduct an in-depth analysis of examples where re-ranking returns the correct program, but
Error Type
Spider
Bird
GSM8K
Iter. Correct.
Tree Search
Iter. Correct.
T... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
71b4ab26-4783-4fad-9a42-79c97dceb370 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 7.3 Analysis
5% of the total 37 errors) of them. However, not only does the discriminator fail to trigger early exits, but it also assigns a higher score for wrong SQL queries in new iterations. Consequently, these erroneous SQL queries ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
b472e7bd-a845-409b-b7dc-4cdbdcd15417 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## 8 Conclusions
This paper presents a thorough investigation into the relationship between discrimination accuracy and performance of planning methods in language agents. Through comprehensive experiments on text-to-SQL parsing and mathema... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
d342a4fc-7243-41e2-8dc6-b80bebe98966 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Limitations
Experiments with Other Models. In this study, we focus on studying the generation and discrimination of instruction-tuned LLMs that have seen code data during pre-training. This consideration is because: **(a)** They may have... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
9062ec53-0b87-44c6-a9f7-a2bc461651a1 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Acknowledgements
We would like to thank colleagues from the OSU
NLP group for their thoughtful comments. This research was supported in part by a sponsored award from Cisco Research, NSF IIS-1815674, NSF CA- REER #1942980, NSF OAC-211260... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c8d75405-7814-45fd-8cf5-ff6208e802c0 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## A Implementation Details A.1 Text-To-Sql Parsing Evaluation Sets
For text-to-SQL parsing, we sub-sample the development splits of each dataset, Spider and Bird, following three steps: **(1)** categorize development set examples by diffic... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
bfb23464-06ef-45c8-96b7-3c339dfe5615 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## A.2 Intrinsic Evaluation Data
To evaluate LLMs' discrimination performance, we reuse the generation results from our oraclesimulation experiments (Section 6). Specifically, we use the evaluation scripts to re-label the generated programs... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c17cfdbf-7972-43d5-80b5-d5ba61aba922 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## A.3 Prompting And Training Llms
Prompting the Generator LM. We prompt our generator LM, CodeLlama-13B-Instruct, with temperature-based sampling for different program suggestions (Section 3.1). We use the model checkpoint and generation f... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
2c272951-43ee-4669-be8f-f1f4dfc0347f | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## A.3 Prompting And Training Llms
the API of (OpenAI, 2022, 2023).
The specific model versions are gpt-3.5-turbo-1106 and gpt-4-1106-preview, respectively. We prompt the LLMs to generate one token and leverage the top_logprobs request t... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
19634746-a77a-40e8-9aeb-6314f041c1dc | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## A.4 Implemendation Of Oracle Discriminator
For text-to-SQL parsing, our oracle uses the first five rows in execution results of the predicted and gold SQL query and calculate the table cell overlap. More specifically, the calculation is ... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4b3a74a0-4647-4a44-9cef-183699d27469 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## B Mcnemar'S Test For Statistical Significance
We measure the statistical significance of performance gains using the exact McNemar's Test4 (Mc-
Nemar, 1947). We choose the test's exact binomial version because our sample sizes are relati... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
d4b753e3-5e6f-4ff5-9fc7-1a400a9ae827 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## C Prompt Examples
Given database schema and a question in natural language, generate the corresponding SQL query.
-- Database climbing:
-- Table mountain: mountain_id, name, height, prominence, range, country
-- Table climber: climber_... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
460d6d79-43e2-45f0-a9be-ce3cd9fd3e67 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## C Prompt Examples
question: Is the SQL correct given the utterance?
-- Utterance: How many different countries are all the swimmers from?
-- SQL:
SELECT COUNT(DISTINCT nationality) FROM swimmer;
-- Answer: Yes
-- Utterance: How many... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
e66934f4-1bca-492e-baab-bed5276f8ceb | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Given questions in the comment, use python programs to produce the correct answers with the 'answer' variable. ## James takes 2 Tylenol tablets that are 375 mg each, every 6 hours.
How many mg does he take a day? ## Python Program:
mg_ty... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
40ba4721-47d1-4cda-ae6f-0092a1d8c4e8 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Gloria is shoe shopping when she comes across a pair of boots that fit her shoe
budget.
However, she has to choose between the boots and two pairs of high heels that
together cost five dollars less than the boots. If one pair of heels co... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
b24d002e-a21c-4517-913d-599c82b4ec14 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Buggy Python Program:
price_boots = 50
price_heels = 33
price_heels_twice = 2 * price_heels
price_heels_total = price_heels + price_heels_twice
price_boots_difference = price_boots - price_heels_total
answer = price_boots_difference | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
504e3178-ef4b-4076-9058-9dbc02672a55 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## James takes 2 Tylenol tablets that are 375 mg each,
every 6 hours.
How many mg
does he take a day? | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
f78e9e5e-476c-4dc0-8f95-72df8f083154 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
mg_tylenol_per_tablet = 375
mg_tylenol_taken_each_time = 2 * mg_tylenol_per_tablet
hours_per_day = 24
times_per_day = hours_per_day / 6
mg_each_day = mg_tylenol_taken_each_time * times_per_day
answer = mg_each_day | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
1f7aca51-9a54-435b-84e1-a8412f2758c8 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## James takes 2 Tylenol tablets that are 375 mg each,
every 6 hours.
How many mg
does he take a day? | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
c450fa86-c3e2-4c0e-a225-35ed40e3e01d | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
mg_per_tablet = 375
n_tablets_per_day = 2
n_tablets_per_6hrs = n_tablets_per_day / 6
mg_per_6hrs = mg_per_tablet * n_tablets_per_6hrs
answer = mg_per_6hrs | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
0563fee2-ba9b-414e-9895-fc6c0e32774b | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## There were 63 Easter eggs in the yard.
Hannah found twice as many as Helen.
How
many Easter eggs did Hannah find? | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
aa9b5f67-14c4-454e-9cfa-0e377a16f915 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
n_easter_eggs = 63
unit_times = 2
total_units = unit_times + 1
n_easter_eggs_per_unit = n_easter_eggs / total_units
n_easter_eggs_helen = n_easter_eggs_per_unit * 1
n_easter_eggs_hannah = n_easter_eggs_per_unit * 2
answer... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
333ee4e2-44e3-4510-ad34-56510a33a47b | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## There were 63 Easter eggs in the yard.
Hannah found twice as many as Helen.
How
many Easter eggs did Hannah find? | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
012933d6-cea7-4431-8c57-830cc04c7426 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
eggs_in_yard = 63
eggs_found_by_hannah = 2 * eggs_in_yard
eggs_found_by_helen = eggs_found_by_hannah / 2
answer = eggs_found_by_hannah | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
439382a2-2622-4509-9d92-36f4d27aeaf0 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Gloria is shoe shopping when she comes across a pair of boots that fit her shoe
budget.
However, she has to choose between the boots and two pairs of high heels that
together cost five dollars less than the boots. If one pair of heels co... | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
4bbf5650-09ca-423f-a7ef-d749c03ea14f | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
price_boots = 50
price_heels = 33
price_heels_twice = 2 * price_heels
price_heels_total = price_heels + price_heels_twice
price_boots_difference = price_boots - price_heels_total
answer = price_boots_difference | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
a957a4f0-4aab-44a3-866d-4271a0ccf631 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## James takes 2 Tylenol tablets that are 375 mg each,
every 6 hours.
How many mg
does he take a day? | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
a2cf206a-3971-47a7-8268-7af8e0fca42a | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
mg_tylenol_per_tablet = 375
mg_tylenol_taken_each_time = 2 * mg_tylenol_per_tablet
hours_per_day = 24
times_per_day = hours_per_day / 6
mg_each_day = mg_tylenol_taken_each_time * times_per_day
answer = mg_each_day | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
a6b0c03b-4f1c-4a14-b5ec-26926091b84a | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## James takes 2 Tylenol tablets that are 375 mg each,
every 6 hours.
How many mg
does he take a day? | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
6b12e095-dae1-4c1f-8de5-66ea067205f4 | # When Is Tree Search Useful For Llm Planning? It Depends On The Discriminator
## Python Program:
mg_per_tablet = 375
n_tablets_per_day = 2
n_tablets_per_6hrs = n_tablets_per_day / 6
mg_per_6hrs = mg_per_tablet * n_tablets_per_6hrs
answer = mg_per_6hrs | {
"creation_datetime": "2024-03-04",
"file_name": "2402.10890v1.md",
"file_path": "paper_data/2402.10890v1.md",
"file_size": 67960,
"file_type": null,
"last_accessed_datetime": "2024-03-04",
"last_modified_datetime": "2024-02-22"
} |
End of preview. Expand in Data Studio
AutoRAG evaluation dataset
Made with 2024 LLM resesarch articles (papers)
This dataset is an example for AutoRAG. You can directly use this dataset for optimizng and benchmarking your RAG setup in AutoRAG.
How this dataset created?
This dataset is 100% synthetically generated by GPT-4 and Marker Inc. technology.
At first, we collected 110 latest LLM papers at arxiv.
We used Marker OCR model to extract texts.
And chunk it using MarkdownSplitter and TokenSplitter from Langchain.
For more quality, we delete all References in the research articles.
And then, it randomly select 520 passages from chunked corpus for generating question.
At last, our custom pipeline generates various and unique questions with GPT-4.
Acknowledgements
This dataset's corpus is originated various LLM related research articles on arixv. Marker Inc. do not have copyright or any rights about corpus content itself.
Plus, this is a alpha version of our evaluation data generation pipeline without human verification, so its quality might be lower than human-generated dataset.
- Downloads last month
- 37