A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations

• URL: http://arxiv.org/abs/2407.04069v2
• Date: Thu, 3 Oct 2024 13:51:53 GMT
• Title: A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
• Authors: Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang,
• Abstract summary: Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities. We systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust.
• Score: 35.12731651234186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have recently gained significant attention due to their remarkable capabilities in performing diverse tasks across various domains. However, a thorough evaluation of these models is crucial before deploying them in real-world applications to ensure they produce reliable performance. Despite the well-established importance of evaluating LLMs in the community, the complexity of the evaluation process has led to varied evaluation setups, causing inconsistencies in findings and interpretations. To address this, we systematically review the primary challenges and limitations causing these inconsistencies and unreliable evaluations in various steps of LLM evaluation. Based on our critical review, we present our perspectives and recommendations to ensure LLM evaluations are reproducible, reliable, and robust.

Related papers

• ReIFE: Re-evaluating Instruction-Following Evaluation [105.75525154888655]
  We present a thorough meta-evaluation of instruction following, including 25 base LLMs and 15 proposed evaluation protocols. Our evaluation allows us to identify the best-performing base LLMs and evaluation protocols with a high degree of robustness.
  arXiv  Detail & Related papers  (2024-10-09T17:14:50Z)
• Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks [3.773596042872403]
  Large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Various frameworks have emerged as noteworthy contributions to the field, offering comprehensive evaluation tests and benchmarks. This paper provides an exploration and critical analysis of some of these evaluation methodologies, shedding light on their strengths, limitations, and impact on advancing the state-of-the-art in natural language processing.
  arXiv  Detail & Related papers  (2024-07-29T03:37:14Z)
• Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework [75.81096662788254]
  Large Language Models (LLMs) are scalable and economical evaluators. The question of how reliable these evaluators are has emerged as a crucial research question. 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)
• CriticEval: Evaluating Large Language Model as Critic [110.29766259843453]
  CriticEval is a novel benchmark designed to comprehensively and reliably evaluate critique ability of Large Language Models. To ensure the comprehensiveness, CriticEval evaluates critique ability from four dimensions across nine diverse task scenarios. To ensure the reliability, a large number of critiques are annotated to serve as references.
  arXiv  Detail & Related papers  (2024-02-21T12:38:59Z)
• F-Eval: Assessing Fundamental Abilities with Refined Evaluation Methods [102.98899881389211]
  We propose F-Eval, a bilingual evaluation benchmark to evaluate the fundamental abilities, including expression, commonsense and logic. For reference-free subjective tasks, we devise new evaluation methods, serving as alternatives to scoring by API models.
  arXiv  Detail & Related papers  (2024-01-26T13:55:32Z)
• Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems [0.0]
  The rise of generative artificial intelligence, particularly Large Language Models (LLMs), has intensified the imperative to scrutinize fairness alongside accuracy. Recent studies have begun to investigate fairness evaluations for LLMs within domains such as recommendations. Yet, the degree to which current fairness evaluation frameworks account for personalization remains unclear.
  arXiv  Detail & Related papers  (2024-01-08T17:57:29Z)
• 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)
• 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)

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.