Re-evaluating Open-ended Evaluation of Large Language Models

• URL: http://arxiv.org/abs/2502.20170v1
• Date: Thu, 27 Feb 2025 15:07:47 GMT
• Title: Re-evaluating Open-ended Evaluation of Large Language Models
• Authors: Siqi Liu, Ian Gemp, Luke Marris, Georgios Piliouras, Nicolas Heess, Marc Lanctot,
• Abstract summary: We show that the current Elo-based rating systems can be susceptible to and even reinforce biases in data, intentional or accidental.<n>We propose evaluation as a 3-player game, and introduce novel game-theoretic solution concepts to ensure robustness to redundancy.
• Score: 50.23008729038318
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evaluation has traditionally focused on ranking candidates for a specific skill. Modern generalist models, such as Large Language Models (LLMs), decidedly outpace this paradigm. Open-ended evaluation systems, where candidate models are compared on user-submitted prompts, have emerged as a popular solution. Despite their many advantages, we show that the current Elo-based rating systems can be susceptible to and even reinforce biases in data, intentional or accidental, due to their sensitivity to redundancies. To address this issue, we propose evaluation as a 3-player game, and introduce novel game-theoretic solution concepts to ensure robustness to redundancy. We show that our method leads to intuitive ratings and provide insights into the competitive landscape of LLM development.

Related papers

• Meta-Evaluating Local LLMs: Rethinking Performance Metrics for Serious Games [3.725822359130832]
  Large Language Models (LLMs) are increasingly being explored as evaluators in serious games. This study investigates the reliability of five small-scale LLMs when assessing player responses in textitEn-join, a game that simulates decision-making within energy communities. Our results highlight the strengths and limitations of each model, revealing trade-offs between sensitivity, specificity, and overall performance.
  arXiv  Detail & Related papers  (2025-04-13T10:46:13Z)
• Diverging Preferences: When do Annotators Disagree and do Models Know? [92.24651142187989]
  We develop a taxonomy of disagreement sources spanning 10 categories across four high-level classes. We find that the majority of disagreements are in opposition with standard reward modeling approaches. We develop methods for identifying diverging preferences to mitigate their influence on evaluation and training.
  arXiv  Detail & Related papers  (2024-10-18T17:32:22Z)
• A Normative Framework for Benchmarking Consumer Fairness in Large Language Model Recommender System [9.470545149911072]
  This paper proposes a normative framework to benchmark consumer fairness in LLM-powered recommender systems. We argue that this gap can lead to arbitrary conclusions about fairness. Experiments on the MovieLens dataset on consumer fairness reveal fairness deviations in age-based recommendations.
  arXiv  Detail & Related papers  (2024-05-03T16:25:27Z)
• Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform [64.76104135495576]
  We propose a novel anonymous crowd-sourcing evaluation platform, BingJian, for large language models. Through this platform, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities.
  arXiv  Detail & Related papers  (2024-03-13T07:31:20Z)
• 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)
• GPTBIAS: A Comprehensive Framework for Evaluating Bias in Large Language Models [83.30078426829627]
  Large language models (LLMs) have gained popularity and are being widely adopted by a large user community. The existing evaluation methods have many constraints, and their results exhibit a limited degree of interpretability. We propose a bias evaluation framework named GPTBIAS that leverages the high performance of LLMs to assess bias in models.
  arXiv  Detail & Related papers  (2023-12-11T12:02:14Z)
• MMBench: Is Your Multi-modal Model an All-around Player? [114.45702807380415]
  We propose MMBench, a benchmark for assessing the multi-modal capabilities of vision-language models. MMBench is meticulously curated with well-designed quality control schemes. MMBench incorporates multiple-choice questions in both English and Chinese versions.
  arXiv  Detail & Related papers  (2023-07-12T16:23:09Z)
• Style Over Substance: Evaluation Biases for Large Language Models [17.13064447978519]
  This study investigates the behavior of crowd-sourced and expert annotators, as well as large language models (LLMs) Our findings reveal a concerning bias in the evaluation process, as answers with factual errors are rated more favorably than answers that are too short or contained grammatical errors. We propose independently evaluating machine-generated text across multiple dimensions, rather than merging all the evaluation aspects into a single score.
  arXiv  Detail & Related papers  (2023-07-06T14:42:01Z)
• Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model [83.83064559894989]
  A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. We develop a new estimator that mitigates the problems of the two most popular off-policy estimators for rankings. In particular, the new estimator, called INTERPOL, addresses the bias of a potentially misspecified position-based model.
  arXiv  Detail & Related papers  (2022-10-15T17:22:30Z)

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.