Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews

• URL: http://arxiv.org/abs/2403.07183v2
• Date: Sat, 15 Jun 2024 05:23:06 GMT
• Title: Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
• Authors: Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou,
• Abstract summary: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM) Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level.
• Score: 51.453135368388686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

Related papers

• Is Your Paper Being Reviewed by an LLM? Investigating AI Text Detectability in Peer Review [8.606381080620789]
  We investigate the ability of existing AI text detection algorithms to distinguish between peer reviews written by humans and different state-of-the-art LLMs. Our analysis shows that existing approaches fail to identify many GPT-4o written reviews without also producing a high number of false positive classifications. We propose a new detection approach which surpasses existing methods in the identification of GPT-4o written peer reviews at low levels of false positive classifications.
  arXiv  Detail & Related papers  (2024-10-03T22:05:06Z)
• Beyond Turing Test: Can GPT-4 Sway Experts' Decisions? [14.964922012236498]
  This paper explores how generated text impacts readers' decisions, focusing on both amateur and expert audiences. Our findings indicate that GPT-4 can generate persuasive analyses affecting the decisions of both amateurs and professionals. The results highlight a high correlation between real-world evaluation through audience reactions and the current multi-dimensional evaluators commonly used for generative models.
  arXiv  Detail & Related papers  (2024-09-25T07:55:36Z)
• Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? [52.00419656272129]
  We conducted an experiment during the 2023 International Conference on Machine Learning (ICML) We received 1,342 rankings, each from a distinct author, pertaining to 2,592 submissions. We focus on the Isotonic Mechanism, which calibrates raw review scores using author-provided rankings.
  arXiv  Detail & Related papers  (2024-08-24T01:51:23Z)
• TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization [29.49641083851667]
  We propose a new evaluation benchmark on topic-focused dialogue summarization, generated by LLMs of varying sizes. We provide binary sentence-level human annotations of the factual consistency of these summaries along with detailed explanations of factually inconsistent sentences.
  arXiv  Detail & Related papers  (2024-02-20T18:58:49Z)
• Reasons to Reject? Aligning Language Models with Judgments [72.39858230784002]
  We explore the use of language feedback to align large language models (LLMs) We propose Contrastive Unlikelihood Training (CUT) that allows for fine-grained inappropriate content detection and correction based on judgments. Our results show CUT can beat the 175B DaVinci003 and surpass the best baseline by 50.84 points on AlpacaEval.
  arXiv  Detail & Related papers  (2023-12-22T10:29:43Z)
• ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning [7.457517083017178]
  Large language models (LLMs) are used for evaluation of text generated by humans and AI alike. Despite their utility, LLMs exhibit distinct failure modes, necessitating a thorough audit and improvement of their text evaluation capabilities. Here we introduce ALLURE, a systematic approach to Auditing Large Language Models Understanding and Reasoning Errors.
  arXiv  Detail & Related papers  (2023-09-24T17:15:58Z)
• L-Eval: Instituting Standardized Evaluation for Long Context Language Models [91.05820785008527]
  We propose L-Eval to institute a more standardized evaluation for long context language models (LCLMs) We build a new evaluation suite containing 20 sub-tasks, 508 long documents, and over 2,000 human-labeled query-response pairs. Results show that popular n-gram matching metrics generally can not correlate well with human judgment.
  arXiv  Detail & Related papers  (2023-07-20T17:59:41Z)
• Statistical Knowledge Assessment for Large Language Models [79.07989821512128]
  Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? We propose KaRR, a statistical approach to assess factual knowledge for LLMs. Our results reveal that the knowledge in LLMs with the same backbone architecture adheres to the scaling law, while tuning on instruction-following data sometimes compromises the model's capability to generate factually correct text reliably.
  arXiv  Detail & Related papers  (2023-05-17T18:54:37Z)
• Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models [57.80514758695275]
  Using large language models (LLMs) for assessing the quality of machine translation (MT) achieves state-of-the-art performance at the system level. We propose a new prompting method called textbftextttError Analysis Prompting (EAPrompt) This technique emulates the commonly accepted human evaluation framework - Multidimensional Quality Metrics (MQM) and textitproduces explainable and reliable MT evaluations at both the system and segment level.
  arXiv  Detail & Related papers  (2023-03-24T05:05:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.