Awes, Laws, and Flaws From Today's LLM Research

• URL: http://arxiv.org/abs/2408.15409v3
• Date: Fri, 30 May 2025 22:55:40 GMT
• Title: Awes, Laws, and Flaws From Today's LLM Research
• Authors: Adrian de Wynter,
• Abstract summary: We assess over 2,000 research works released between 2020 and 2024 based on criteria typical of what is considered good research.<n>We find multiple trends, such as declines in ethics disclaimers, a rise of LLMs as evaluators, and an increase on claims of LLM reasoning abilities without leveraging human evaluation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We perform a critical examination of the scientific methodology behind contemporary large language model (LLM) research. For this we assess over 2,000 research works released between 2020 and 2024 based on criteria typical of what is considered good research (e.g. presence of statistical tests and reproducibility), and cross-validate it with arguments that are at the centre of controversy (e.g., claims of emergent behaviour). We find multiple trends, such as declines in ethics disclaimers, a rise of LLMs as evaluators, and an increase on claims of LLM reasoning abilities without leveraging human evaluation. We note that conference checklists are effective at curtailing some of these issues, but balancing velocity and rigour in research cannot solely rely on these. We tie all these findings to findings from recent meta-reviews and extend recommendations on how to address what does, does not, and should work in LLM research.

Related papers

• LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations [29.031539043555362]
  Large Language Models (LLMs) are increasingly used to evaluate information systems. Recent studies suggest that LLM-based evaluations often align with human judgments. This paper examines scenarios where LLM-evaluators may falsely indicate success.
  arXiv  Detail & Related papers  (2025-04-27T02:14:21Z)
• ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition [67.26124739345332]
  Large language models (LLMs) have demonstrated potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined. We introduce the first large-scale benchmark for evaluating LLMs with a near-sufficient set of sub-tasks of scientific discovery. We develop an automated framework that extracts critical components - research questions, background surveys, inspirations, and hypotheses - from scientific papers.
  arXiv  Detail & Related papers  (2025-03-27T08:09:15Z)
• A Survey on Enhancing Causal Reasoning Ability of Large Language Models [15.602788561902038]
  Large language models (LLMs) have recently shown remarkable performance in language tasks and beyond. LLMs still face challenges in handling tasks that require robust causal reasoning ability, such as health-care and economic analysis. This paper systematically reviews literature on how to strengthen LLMs' causal reasoning ability.
  arXiv  Detail & Related papers  (2025-03-12T12:20:31Z)
• LLM4SR: A Survey on Large Language Models for Scientific Research [15.533076347375207]
  Large Language Models (LLMs) offer unprecedented support across various stages of the research cycle.<n>This paper presents the first systematic survey dedicated to exploring how LLMs are revolutionizing the scientific research process.
  arXiv  Detail & Related papers  (2025-01-08T06:44:02Z)
• LLM-based relevance assessment still can't replace human relevance assessment [12.829823535454505]
  Recent studies suggest that large language models (LLMs) for relevance assessment in information retrieval provide comparable evaluations to human judgments. Upadhyay et al. claim that LLM-based relevance assessments can fully replace traditional human relevance assessments in TREC-style evaluations. This paper critically examines this claim, highlighting practical and theoretical limitations that undermine the validity of this conclusion.
  arXiv  Detail & Related papers  (2024-12-22T20:45:15Z)
• Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review [66.73247554182376]
  Large language models (LLMs) have led to their integration into peer review. The unchecked adoption of LLMs poses significant risks to the integrity of the peer review system. We show that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings.
  arXiv  Detail & Related papers  (2024-12-02T16:55:03Z)
• Bias in Large Language Models: Origin, Evaluation, and Mitigation [4.606140332500086]
  Large Language Models (LLMs) have revolutionized natural language processing, but their susceptibility to biases poses significant challenges. This comprehensive review examines the landscape of bias in LLMs, from its origins to current mitigation strategies. Ethical and legal implications of biased LLMs are discussed, emphasizing potential harms in real-world applications such as healthcare and criminal justice.
  arXiv  Detail & Related papers  (2024-11-16T23:54:53Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• LLMs as Research Tools: A Large Scale Survey of Researchers' Usage and Perceptions [20.44227547555244]
  Large language models (LLMs) have led many researchers to consider their usage for scientific work. We present the first large-scale survey of 816 verified research article authors. We find that 81% of researchers have already incorporated LLMs into different aspects of their research workflow.
  arXiv  Detail & Related papers  (2024-10-30T04:25:23Z)
• A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions [0.0]
  Large Language Models (LLMs) have revolutionized various applications in natural language processing (NLP) by providing unprecedented text generation, translation, and comprehension capabilities. Their widespread deployment has brought to light significant concerns regarding biases embedded within these models. This paper presents a comprehensive survey of biases in LLMs, aiming to provide an extensive review of the types, sources, impacts, and mitigation strategies related to these biases.
  arXiv  Detail & Related papers  (2024-09-24T19:50:38Z)
• AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment [37.985947029716016]
  Large language models (LLMs) have shown advanced understanding capabilities but may inherit human biases from their training data. We investigated whether LLMs are influenced by the threshold priming effect in relevance judgments.
  arXiv  Detail & Related papers  (2024-09-24T12:23:15Z)
• Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers [90.26363107905344]
  Large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery. No evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas.
  arXiv  Detail & Related papers  (2024-09-06T08:25:03Z)
• Categorical Syllogisms Revisited: A Review of the Logical Reasoning Abilities of LLMs for Analyzing Categorical Syllogism [62.571419297164645]
  This paper provides a systematic overview of prior works on the logical reasoning ability of large language models for analyzing categorical syllogisms. We first investigate all the possible variations for the categorical syllogisms from a purely logical perspective. We then examine the underlying configurations (i.e., mood and figure) tested by the existing datasets.
  arXiv  Detail & Related papers  (2024-06-26T21:17:20Z)
• LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing [106.45895712717612]
  Large language models (LLMs) have shown remarkable versatility in various generative tasks. This study focuses on the topic of LLMs assist NLP Researchers. To our knowledge, this is the first work to provide such a comprehensive analysis.
  arXiv  Detail & Related papers  (2024-06-24T01:30:22Z)
• DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators.<n>The question of how reliable these evaluators are has emerged as a crucial research question.<n>We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
  arXiv  Detail & Related papers  (2024-05-24T08:12:30Z)
• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
  Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law. We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies. We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
  arXiv  Detail & Related papers  (2024-05-02T22:43:02Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work. ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• Mapping the Increasing Use of LLMs in Scientific Papers [99.67983375899719]
  We conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers.
  arXiv  Detail & Related papers  (2024-04-01T17:45:15Z)
• Post Turing: Mapping the landscape of LLM Evaluation [22.517544562890663]
  This paper traces the historical trajectory of Large Language Models (LLMs) evaluations, from the foundational questions posed by Alan Turing to the modern era of AI research. We emphasize the pressing need for a unified evaluation system, given the broader societal implications of these models. This work serves as a call for the AI community to collaboratively address the challenges of LLM evaluation, ensuring their reliability, fairness, and societal benefit.
  arXiv  Detail & Related papers  (2023-11-03T17:24:50Z)
• Collaborative Evaluation: Exploring the Synergy of Large Language Models and Humans for Open-ended Generation Evaluation [71.76872586182981]
  Large language models (LLMs) have emerged as a scalable and cost-effective alternative to human evaluations. We propose a Collaborative Evaluation pipeline CoEval, involving the design of a checklist of task-specific criteria and the detailed evaluation of texts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)
• Large Language Model Alignment: A Survey [42.03229317132863]
  The potential of large language models (LLMs) is undeniably vast; however, they may yield texts that are imprecise, misleading, or even detrimental. This survey endeavors to furnish an extensive exploration of alignment methodologies designed for LLMs. We also probe into salient issues including the models' interpretability, and potential vulnerabilities to adversarial attacks.
  arXiv  Detail & Related papers  (2023-09-26T15:49:23Z)
• A Survey on Evaluation of Large Language Models [87.60417393701331]
  Large language models (LLMs) are gaining increasing popularity in both academia and industry. This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
  arXiv  Detail & Related papers  (2023-07-06T16:28:35Z)
• Understanding Social Reasoning in Language Models with Language Models [34.068368860882586]
  We present a novel framework for generating evaluations with Large Language Models (LLMs) by populating causal templates. We create a new social reasoning benchmark (BigToM) for LLMs which consists of 25 controls and 5,000 model-written evaluations. We find that human participants rate the quality of our benchmark higher than previous crowd-sourced evaluations and comparable to expert-written evaluations.
  arXiv  Detail & Related papers  (2023-06-21T16:42:15Z)

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.