GitBag/Reviewer2_Mp
Text Generation • Updated • 617
Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
venue stringclasses 2
values | paper_content stringlengths 7.54k 83.7k | prompt stringlengths 161 2.5k | format stringclasses 5
values | review stringlengths 293 9.84k |
|---|---|---|---|---|
ICLR | Title
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Abstract
From the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should reflect those v... | 1. What is the focus and contribution of the paper on disentangled directions for pretrained models?
2. What are the strengths of the proposed framework, particularly in terms of its model-agnostic nature and ability to mitigate poor generation quality?
3. What are the weaknesses of the approach, especially regarding t... | Summary Of The Paper
Review | Summary Of The Paper
This paper presents a framework to model disentangled directions for pretrained models. Such an approach mitigates the problems with poor generation quality arising while training models with additional regularization terms to force disentanglement. The underlying idea is contrastive-based: similar... |
ICLR | Title
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Abstract
From the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should reflect those v... | 1. What is the focus and contribution of the paper on disentanglement?
2. What are the strengths of the proposed approach, particularly in terms of its ability to achieve SOTA results and ensure good generation quality?
3. What are the weaknesses of the paper, especially regarding the proposed method's flaws and the us... | Summary Of The Paper
Review | Summary Of The Paper
This paper proposes DisCo, a framework that learns disentangled representations from pretrained entangled generative models. Extensive experimental results show that DisCo outperforms many baselines in both quantitative and qualitative evaluations.
Review
Pros:
The proposed method is novel and ach... |
ICLR | Title
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View
Abstract
From the intuitive notion of disentanglement, the image variations corresponding to different factors should be distinct from each other, and the disentangled representation should reflect those v... | 1. What is the focus of the paper regarding representation learning?
2. What are the strengths of the proposed method, particularly its simplicity and effectiveness?
3. What are the weaknesses of the paper, such as the lack of explanation for choosing semantically meaningful directions?
4. How does the reviewer assess ... | Summary Of The Paper
Review | Summary Of The Paper
The paper proposes a novel representation learning technique to disentangle the latent space of pre-trained generative models, by discovering semantically meaningful directions in them.
The method trains a navigator and a delta-contrastor network, which consists of 2 encoders sharing weights. First... |
ICLR | "Title\nLearning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrasti(...TRUNCATED) | "1. What is the focus of the paper regarding disentanglement learning?\n2. What are the strengths of(...TRUNCATED) | Summary Of The Paper
Review | "Summary Of The Paper\nThis paper proposes to learn disentangled representations via contrastive lea(...TRUNCATED) |
ICLR | "Title\nProMP: Proximal Meta-Policy Search\nAbstract\nCredit assignment in Meta-reinforcement learni(...TRUNCATED) | "1. What are the differences in gradient calculation between the original MAML and E-MAML?\n2. How d(...TRUNCATED) | Review | "Review\n\nIn this paper, the authors investigate the gradient calculation in the original MAML (Fin(...TRUNCATED) |
ICLR | "Title\nProMP: Proximal Meta-Policy Search\nAbstract\nCredit assignment in Meta-reinforcement learni(...TRUNCATED) | "1. What is the focus of the paper regarding meta-reinforcement learning?\n2. What are the strengths(...TRUNCATED) | Review | "Review\nIn this paper, the author proposed an efficient surrogate loss for estimating Hessian in t(...TRUNCATED) |
ICLR | "Title\nProMP: Proximal Meta-Policy Search\nAbstract\nCredit assignment in Meta-reinforcement learni(...TRUNCATED) | "1. What are the main contributions and improvements introduced by the paper regarding MAML and E-MA(...TRUNCATED) | Review | "Review\nThe paper first examines the objective function optimized in MAML and E-MAML and interprets(...TRUNCATED) |
ICLR | "Title\nPutting Theory to Work: From Learning Bounds to Meta-Learning Algorithms\nAbstract\nMost of (...TRUNCATED) | "1. What is the focus of the reviewed paper regarding meta-learning?\n2. What are the concerns regar(...TRUNCATED) | Review | "Review\n########################################################################## Summary:\nThe pa(...TRUNCATED) |
ICLR | "Title\nPutting Theory to Work: From Learning Bounds to Meta-Learning Algorithms\nAbstract\nMost of (...TRUNCATED) | "1. What are the limitations of the paper regarding its application of meta-learning theory in few-s(...TRUNCATED) | Review | "Review\nThe main motivation of this paper is based on the theoretical results of meta-learning. To (...TRUNCATED) |
ICLR | "Title\nPutting Theory to Work: From Learning Bounds to Meta-Learning Algorithms\nAbstract\nMost of (...TRUNCATED) | "1. What are the contributions and novel aspects of the paper regarding meta-learning algorithms?\n2(...TRUNCATED) | Review | "Review\nTo improve the practical performance of meta-learning algorithms, this paper proposes two r(...TRUNCATED) |
End of preview. Expand in Data Studio
Cleaned Review Dataset for Reviewer2
This is a cleaned version of our dataset and can be directly used for fine-tuning. The raw data files including metadata for each paper is in this directory.
- venue: venue of the paper;
- paper_content: content of the paper divided into sections
- prompt: prompt generated for the review based on our PGE pipeline
- format: the format of the review
- review: human-written review for the paper
Dataset Sources
We incorporate parts of the PeerRead and NLPeer datasets along with an update-to-date crawl from ICLR and NeurIPS on OpenReview and NeurIPS Proceedings.
Citation
If you find this dataset useful in your research, please cite the following paper:
@misc{gao2024reviewer2,
title={Reviewer2: Optimizing Review Generation Through Prompt Generation},
author={Zhaolin Gao and Kianté Brantley and Thorsten Joachims},
year={2024},
eprint={2402.10886},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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