Summary ARIES Corpus of Scientific Paper Edits arxiv.org
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The text excerpt is from the document ARIES Corpus of Scientific Paper Edits, which discusses precision, recall, and F1 scores of comment-edit pairs, models used in the experiment, challenges of comment-source alignment, edit extraction and GPT edit generation, and observations about types of comments found in reviews.
Slides
Slide Presentation (8 slides)
Key Points
- The ARIES Corpus of Scientific Paper Edits provides information about precision, recall, and F1 scores of comment-edit pairs.
- Different models such as DeBERTa, LinkBERT, Cross-encoder, Specter, and Bi-encoder were used in the experiment.
- GPT-4 generated edits that were comparable to real edits in compliance and technical details, but lacked specific information and relied on paraphrasing.
- GPT-4 outperformed smaller locally-trained models in the comment-edit alignment task, particularly in the addition-only edits setting.
- The synthetic data used for training had high precision but low recall, while manually-annotated data was more comprehensive.
- None of the models reached human-level performance in the comment-edit alignment task.
- The ARIES Corpus is a dataset and code available for generating edits directly from feedback, aiming to develop systems that can reason about scientific content and assist in revising papers.
- The task of revising scientific papers based on peer feedback is challenging and requires deep scientific knowledge and reasoning.
Summary
674 word summary
The text excerpt is from the document ARIES Corpus of Scientific Paper Edits. It provides information about precision, recall, and F1 scores of comment-edit pairs. The results are presented in Table 8. The text also mentions different models used in the experiment, such as DeBERTa, LinkBERT, Cross-encoder, Specter, and Bi-encoder. It discusses the challenges of comment-source alignment and the implementation details of the experiments. The text also includes information about edit extraction and GPT edit generation. Additionally, it mentions the prompts used for GPT experiments and compares manually-annotated data with synthetic data. The text concludes with observations about the types of comments found in reviews and provides references to related papers. Fan Zhang, Homa B. Hashemi, Rebecca Hwa, and Ding Machinery conducted a study on the use of large language models (LLMs) in scientific paper editing. They analyzed the outputs of GPT-4, a state-of-the-art LLM, and compared them to real edits made by humans. The study found that while GPT-4 generated edits that were comparable to real edits in terms of compliance and technical details, it often lacked specific information and relied on paraphrasing. The authors suggest that future work should focus on improving the ability of LLMs to access and utilize detailed information, as well as conducting experiments to evaluate their impact on users. Despite its limitations, the study provides insights that can guide future research in this area. The text excerpt is from a document called the ARIES Corpus of Scientific Paper Edits. It includes tables and descriptions of comment-edit pairs, as well as an analysis of the edit generation task. The text discusses factors such as compliance, technical details, and paraphrasing in the generated edits. It also highlights the differences between edits generated by GPT-4 and those made by human authors. The text mentions the challenges of evaluating the correctness and comprehensiveness of the edits and provides insights into the performance of GPT-4 in addressing reviewer comments. The GPT-4 methods outperform smaller locally-trained models in the comment-edit alignment task, particularly in the addition-only edits setting. The F1 scores for different models are presented in Table 2, showing that GPT-4 multi-edit and GPT-4 cross-encoder perform well. The Specter bi-encoder also shows promising results. The models have different strengths and weaknesses, with GPT-4 excelling in macro-F1 and Specter performing well in micro-F1. The BM25-generated baseline also provides competitive results. The synthetic data used for training has high precision but low recall, while the manually-annotated data is more comprehensive. The comment-edit alignment task is challenging, and none of the models reach human-level performance. Different model architectures, including pairwise cross-encoder and multi-edit cross-encoder, are evaluated. Inter-annotator agreement is measured, and the types of actionable comments are analyzed. The document excerpt discusses the ARIES Corpus of Scientific Paper Edits and its construction process. It mentions the tasks of identifying actionable feedback, comment-edit alignment, and edit generation. The document also highlights the challenges and previous research in the field of scientific document revision. The contributions of the study are outlined, along with the evaluation of baseline methods and the potential applications of the tasks. The summary provides a concise overview of the main points discussed in the document. The computational complexity of a model is discussed, along with the importance of aligning work with others and strengthening claims by adding information. The ARIES Corpus is introduced as a dataset and code available for generating edits directly from feedback. Two novel tasks are formulated: responding to reviewer feedback and editing scientific papers based on that feedback. The evaluation of large language models and the realism of the dataset are also addressed. The task of revising scientific papers based on peer feedback is challenging and requires deep scientific knowledge and reasoning. The paper introduces the ARIES Corpus as a testbed for evaluating NLP systems on this task. The goal is to develop systems that can reason about scientific content and assist in revising papers. The paper provides details of the formalization, dataset, and analysis, and hopes that the research will form a foundation for future work in this area.