LLM s Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024)

Abstract: Claim: This work is not advocating the use of LLMs for paper (meta-)reviewing. Instead, wepresent a comparative analysis to identify and distinguish LLM activities from human activities. Two research goals: i) Enable better recognition of instances when someone implicitly uses LLMs for reviewing activities; ii) Increase community awareness that LLMs, and AI in general, are currently inadequate for performing tasks that require a high level of expertise and nuanced judgment.This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time-consuming but also highly expertise-demanding, face increasing challenges as they have to spend more time reading, writing, and reviewing papers. This raises the question: how can LLMs potentially assist researchers in alleviating their heavy workload?This study focuses on the topic of LLMs as NLP Researchers, particularly examining the effectiveness of LLMs in assisting paper (meta-)reviewing and its recognizability. To address this, we constructed the ReviewCritique dataset, which includes two types of information: (i) NLP papers (initial submissions rather than camera-ready) with both human-written and LLM-generated reviews, and (ii) each review comes with “deficiency” labels and corresponding explanations for individual segments, annotated by experts. Using ReviewCritique, this study explores two threads of research questions: (i) “LLMs as Reviewers”, how do reviews generated by LLMs compare with those written by humans in terms of quality and distinguishability? (ii) “LLMs as Metareviewers”, how effectively can LLMs identify potential issues, such as Deficient or unprofessional review segments, within individual paper reviews? To our knowledge, this is the first work to provide such a comprehensive analysis.

## introduction artificial intelligence (ai), particularly through the recent development of large language models (llms), has demonstrated remarkable versatility in tasks such as writing, drawing, and question answering @xcite @xcite . this has led to significant automation of many time-consuming jobs, potentially replacing more roles with ai. interestingly, while researchers, the creators of ai/llms, benefit from llms for simple tasks @xcite , it still takes years to train a qualified researcher due to the domain-specific and expertisedemanding nature of their work. researchers now face increasing challenges with more papers to read, to beat, to write, and to review, resulting in longer and more intensive work hours. this raises the question: how promising is the potential for llms to work as researchers to alleviate their heavy and somewhat unhealthy workload? within the scope of llms assist nlp researchers, this work focuses on how well llms can perform (meta-)reviewing. ai-related conferences and journals are seeing a rapid increase in submissions, making it difficult to recruit enough (meta-)reviewers. paper reviewers must carefully read submissions and provide comments on the overall story, strengths, weaknesses, writing, etc. the meta-reviewer's responsibility is to ensure the accuracy and constructiveness of the individual review. therefore, meta-reviewers are expected to be aware of the submission as well as authors' rebuttals, and then assess individual reviews by identifying unreasonable elements and distilling truly constructive comments. there is a latent trend, though debatable and unacknowledged by reviewers, of llms participating more frequently in the paper-reviewing process. therefore, this work explores two research questions: (i) 'llms as reviewers", how far away or distinguishable are llm-generated paper reviews from humanwritten ones? (ii) "llms as metareviewers", can llms identify deficient review segments by reasoning over the paper submission, other individual reviews, and author rebuttals jointly? to achieve this, we create the reviewcritique dataset, containing: (i) nlp papers (original submissions rather than the final camera-ready) with both human-written and llmgenerated reviews, and (ii) each review annotated by nlp experts (most with ph.d. degrees or area chairing experience) at the sentence level regarding deficiency and professionalism, with explanations. this dataset enables the following analyses. first, for llms as reviewers, we assess the quality of llm-generated reviews by examining subsections or aspects of the review, such as summary, strengths, weaknesses, writing, etc. we propose a novel metric to measure llm-generated review diversity across different papers. our findings indicate that llms generate more deficient review segments than human reviewers and often produce paper-unspecific reviews lacking diversity and constructive feedback. second, for llms as metareviewers, we evaluate llms' ability to identify deficient segments in human-written reviews and provide explana-tions for their judgments. this contrasts with other works treating paper meta-review as a text summarization task given 3+ individual reviews @xcite @xcite . we argue that meta-reviewing should be a knowledgeintensive and reasoning-intensive process, with human meta-reviewers being expected to be careful and responsible. we benchmark both closed-source and open-source llms on this task, finding that even top-tier llms struggle to mimic human experts in assessing individual reviews. overall, our contributions are threefold: (i) the reviewcritique dataset with human-written and llm-generated reviews and fine-grained review deficiency labeling and explanation, serving as a valuable resource for future research on aiassisted peer review and llm benchmarking, (ii) the first quantitative comparison of human-written and llm-generated paper reviews at the sentence level, and (iii) the first analysis of llms' potential as both reviewers and meta-reviewers. by highlighting the strengths and limitations of llms in scientific peer review, our work paves the way for future works on integrating ai for research. ## related work researchers have explored various aspects of ai for reviews. one area of interest is the use of ai to assist in automatically generating peer reviews, such as predicting scores @xcite @xcite and writing reviews @xcite @xcite and meta-reviews @xcite . another line of research focuses on leveraging nlp methods to evaluate the quality of human reviews @xcite @xcite . to facilitate research on ai for peer review, several datasets have been introduced. peerread @xcite , moprd @xcite , and nlpeer @xcite are datasets containing a large number of peer reviews and their corresponding papers but without expert annotations. other datasets focus on specific aspects of peer reviews, such as argument @xcite @xcite @xcite , politeness @xcite , uncertainty detection @xcite , contradictions in review pairs @xcite , and substantiation @xcite . peer review analyze @xcite annotates reviews across four facets: paper section correspondence, aspect, functionality, and significance. however, these datasets are solely based on reviews and none of them are highly expert-demanding. in contrast, reviewcritique is the first dataset to benchmark llms' capability as a responsible meta-reviewer. recently, researchers have also explored the evaluation of llms' deficiency and limitations in automatic paper reviewing tasks @xcite @xcite @xcite . our work differs from previous works in that we provide a quantitative comparison of human-written and llm-generated paper reviews at the sentence level. this fine-grained analysis allows us to identify specific areas where llms excel or struggle in generating high-quality reviews. we also propose a novel metric to measure llm-generated review diversity. ## reviewcritique curation in this section, we detail the process of curating reviewcritique, including the criteria for paper selection, the collection of human-written and llm-generated reviews, the annotation procedure, and the measures taken to ensure data quality. ## experiments we present experimental results and analysis in two threads: llms as reviewers (section 4.1), and llms as metareviewers (section 4.2). ## conclusion this work studied the potential of llms assist nlp researchers, focusing on their roles as reviewers and meta-reviewers. we created reviewcritique , containing both human-written and llm-generated reviews, with detailed deficiency annotations and explanations. our analysis reveals that while llms can generate reviews, they often produce deficient and paper-unspecific segments, lacking the diversity and constructive feedbacks. additionally, even state-of-the-art llms struggle to assess review deficiencies effectively. these findings highlight the current limitations of llms in automating the peer review process.