Datasets:
File size: 6,970 Bytes
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license: other
size_categories:
- 10K<n<100K
task_categories:
- text-generation
license_name: mixed-license
license_link: LICENSE.md
tags:
- llm-agents
- deployment-time-learning
- continual-learning
configs:
- config_name: alfworld
data_files: alfworld/alfworld.jsonl
- config_name: banking77
data_files: banking77/banking77.jsonl
- config_name: bird
data_files: bird/bird.jsonl
- config_name: cmdl
data_files: cmdl/cmdl.jsonl
- config_name: ddxplus
data_files: ddxplus/ddxplus.jsonl
- config_name: lfd
data_files: lfd/lfd.jsonl
- config_name: mud
data_files: mud/mud.jsonl
- config_name: rca
data_files: rca/rca.jsonl
- config_name: scienceworld
data_files: scienceworld/scienceworld.jsonl
- config_name: sentifin
data_files: sentifin/sentifin.jsonl
- config_name: spider
data_files: spider/spider.jsonl
- config_name: 2wiki
data_files: 2wiki/2wiki.jsonl
- config_name: ehr
data_files: ehr/ehr.jsonl
---
# DTLBench
<p align="center">
<a href="https://github.com/guosyjlu/CASCADE">
💻 GitHub Repo
</a> |
<a href="https://physionet.org/content/mimic-iv-ext-dtlbench/1.0.0/">
🫀 DTLBench PhysioNet (To be released)
</a> |
<a href="https://arxiv.org/abs/2605.06702">
📄 Paper
</a>
</p>
DTLBench is a benchmark for **deployment-time learning** of large language model agents. It collects diverse task streams spanning medical diagnosis, legal analysis, operational reasoning, financial prediction, text-to-SQL, embodied decision making, tabular reasoning on EHRs, deep search, etc.
The dataset was introduced in the paper: [CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment](https://arxiv.org/abs/2605.06702).
## Benchmark Overview
- Total tasks: `16` (3 of them will be released through PhysioNet)
- Data format: one JSON object per line (`.jsonl`)
- Primary use case: benchmark streams for deployment-time learning
DTLBench covers three environment styles used in CASCADE:
- `single-turn`: one input, one final answer
- `multi-turn`: sequential interaction with the environment
### Summary statistics of the DTLBench. The maximum steps refer to the maximum number of interaction steps that the environment allows per task.
| **Property** | **Domain** | **Task** | **Dataset** | **Maximum Steps** | **Number of Samples** |
|------------------------------|---------------------|--------------------------------------------------|-------------------|-------------------|-----------------------|
| **Single-turn** | **Medical** | Medical Diagnosis | DDXPlus | 1 | 3136 |
| | | Medication Recommendation | MIMIC-IV-MR | 1 | 2881 |
| | | Medical Specialty Referral | MIMIC-IV-MSR | 1 | 2115 |
| | | Triage Level Prediction | MIMIC-IV-TLP | 1 | 2200 |
| | **Legal** | Multi-Defendant Legal Charge Prediction | MUD | 1 | 1740 |
| | | Penalty Legal Prediction | CMDL | 1 | 2080 |
| | **Financial** | Financial Customer Intent Routing | Banking77 | 1 | 5000 |
| | | Entity-Aware Financial Sentiment Analysis | SEntFiN | 1 | 2299 |
| | **AIOps** | AIOps Root Cause Analysis | RCA | 1 | 2925 |
| | | AIOps Log Fault Diagnosis | LFD | 1 | 3000 |
| | **Coding** | Text-to-SQL | SPIDER | 1 | 2147 |
| | | Knowledge-Augmented Text-to-SQL | BIRD | 1 | 1534 |
| **Multi-turn, Simulated** | **Embodied** | Household Embodied Decision Making | ALFWorld | 30 | 2000 |
| | | Scientific Embodied Decision Making | ScienceWorld | 10-30 | 1857 |
| **Multi-turn, Real-world** | **Information Seeking** | Web-based Deep Search | 2Wiki | 5 | 2500 |
| | **Medical** | Complex Tabular Reasoning on Electronic Health Records | MIMIC-III | 5 | 2500 |
## Data Format
Each config can be loaded independently from Hugging Face, and each task keeps the fields needed by its original environment. All tasks include a `task` field, which is the main query or observation presented to the agent.
## Load the Dataset
Using `datasets`:
```python
from datasets import load_dataset
# Load one task
ddxplus = load_dataset("guosy/DTLBench", "ddxplus")
print(ddxplus["train"][0])
# Load another task
spider = load_dataset("guosy/DTLBench", "spider")
print(spider["train"][0]["task"])
```
Using `huggingface-cli`:
```bash
huggingface-cli download --repo-type dataset guosy/DTLBench --local-dir ./DTLBench
```
## License
DTLBench is a **mixed-license** collection. Each subdataset follows its own original license, and the benchmark authors do not claim additional rights beyond those licenses.
Please see [LICENSE.md](LICENSE.md) and the per-task `LICENSE` files for details. In particular:
- Some tasks are under permissive licenses such as MIT or Apache-2.0
- Some tasks use CC licenses with attribution or share-alike requirements
- Some tasks have unclear or unknown redistribution terms
You are responsible for ensuring your use complies with the license of each individual subdataset.
## Citation
If you use DTLBench, please consider citing our paper:
```
@misc{guo2026cascadecasebasedcontinualadaptation,
title={CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment},
author={Siyuan Guo and Yali Du and Hechang Chen and Yi Chang and Jun Wang},
year={2026},
eprint={2605.06702},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2605.06702},
}
``` |