Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition

• URL: http://arxiv.org/abs/2404.08008v1
• Date: Wed, 10 Apr 2024 01:26:24 GMT
• Title: Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
• Authors: Kehua Feng, Keyan Ding, Kede Ma, Zhihua Wang, Qiang Zhang, Huajun Chen,
• Abstract summary: We propose a sample-efficient human evaluation method based on MAximum Discrepancy (MAD) competition. MAD automatically selects a small set of informative and diverse instructions, each adapted to two LLMs. The pairwise comparison results are then aggregated into a global ranking using the Elo rating system.
• Score: 46.949604465227054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The past years have witnessed a proliferation of large language models (LLMs). Yet, automated and unbiased evaluation of LLMs is challenging due to the inaccuracy of standard metrics in reflecting human preferences and the inefficiency in sampling informative and diverse test examples. While human evaluation remains the gold standard, it is expensive and time-consuming, especially when dealing with a large number of testing samples. To address this problem, we propose a sample-efficient human evaluation method based on MAximum Discrepancy (MAD) competition. MAD automatically selects a small set of informative and diverse instructions, each adapted to two LLMs, whose responses are subject to three-alternative forced choice by human subjects. The pairwise comparison results are then aggregated into a global ranking using the Elo rating system. We select eight representative LLMs and compare them in terms of four skills: knowledge understanding, mathematical reasoning, writing, and coding. Experimental results show that the proposed method achieves a reliable and sensible ranking of LLMs' capabilities, identifies their relative strengths and weaknesses, and offers valuable insights for further LLM advancement.

Related papers

• Reference-Guided Verdict: LLMs-as-Judges in Automatic Evaluation of Free-Form Text [12.879551933541345]
  Large Language Models (LLMs) are capable of generating human-like conversations. Conventional metrics like BLEU and ROUGE are inadequate for capturing the subtle semantics and contextual richness of such generative outputs. We propose a reference-guided verdict method that automates the evaluation process by leveraging multiple LLMs-as-judges.
  arXiv  Detail & Related papers  (2024-08-17T16:01:45Z)
• Decoding Biases: Automated Methods and LLM Judges for Gender Bias Detection in Language Models [47.545382591646565]
  Large Language Models (LLMs) have excelled at language understanding and generating human-level text. LLMs are susceptible to adversarial attacks where malicious users prompt the model to generate undesirable text. In this work, we train models to automatically create adversarial prompts to elicit biased responses from target LLMs.
  arXiv  Detail & Related papers  (2024-08-07T17:11:34Z)
• MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria [49.500322937449326]
  Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries without considering user experiences. We propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge.
  arXiv  Detail & Related papers  (2023-11-23T12:04:25Z)
• 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)
• Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization [66.08074487429477]
  We investigate the stability and reliability of large language models (LLMs) as automatic evaluators for abstractive summarization. We find that while ChatGPT and GPT-4 outperform the commonly used automatic metrics, they are not ready as human replacements.
  arXiv  Detail & Related papers  (2023-05-22T14:58:13Z)
• Evaluating the Performance of Large Language Models on GAOKAO Benchmark [53.663757126289795]
  This paper introduces GAOKAO-Bench, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples. With human evaluation, we obtain the converted total score of LLMs, including GPT-4, ChatGPT and ERNIE-Bot. We also use LLMs to grade the subjective questions, and find that model scores achieve a moderate level of consistency with human scores.
  arXiv  Detail & Related papers  (2023-05-21T14:39:28Z)
• Can Large Language Models Be an Alternative to Human Evaluations? [80.81532239566992]
  Large language models (LLMs) have demonstrated exceptional performance on unseen tasks when only the task instructions are provided. We show that the result of LLM evaluation is consistent with the results obtained by expert human evaluation.
  arXiv  Detail & Related papers  (2023-05-03T07:28:50Z)
• Benchmarking Large Language Models for News Summarization [79.37850439866938]
  Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. We find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability.
  arXiv  Detail & Related papers  (2023-01-31T18:46:19Z)

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.