LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

• URL: http://arxiv.org/abs/2406.18403v1
• Date: Wed, 26 Jun 2024 14:56:13 GMT
• Title: LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks
• Authors: Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, André F. T. Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni,
• Abstract summary: There is an increasing trend towards evaluating NLP models with LLM-generated judgments instead of human judgments. In the absence of a comparison against human data, this raises concerns about the validity of these evaluations. We provide JUDGE-BENCH, a collection of 20 NLP datasets with human annotations.
• Score: 106.09361690937618
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There is an increasing trend towards evaluating NLP models with LLM-generated judgments instead of human judgments. In the absence of a comparison against human data, this raises concerns about the validity of these evaluations; in case they are conducted with proprietary models, this also raises concerns over reproducibility. We provide JUDGE-BENCH, a collection of 20 NLP datasets with human annotations, and comprehensively evaluate 11 current LLMs, covering both open-weight and proprietary models, for their ability to replicate the annotations. Our evaluations show that each LLM exhibits a large variance across datasets in its correlation to human judgments. We conclude that LLMs are not yet ready to systematically replace human judges in NLP.

Related papers

• Systematic Evaluation of LLM-as-a-Judge in LLM Alignment Tasks: Explainable Metrics and Diverse Prompt Templates [10.091146498861333]
  Commercial large language models (LLMs) like GPT-4 have been recently employed to evaluate and compare different alignment approaches. We develop a framework to evaluate, compare, and visualize the reliability and alignment of LLM judges.
  arXiv  Detail & Related papers  (2024-08-23T11:49:01Z)
• 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)
• Large Language Models are Inconsistent and Biased Evaluators [2.136983452580014]
  We show that Large Language Models (LLMs) are biased evaluators as they exhibit familiarity bias and show skewed distributions of ratings. We also found that LLMs are inconsistent evaluators, showing low "inter-sample" agreement and sensitivity to prompt differences that are insignificant to human understanding of text quality.
  arXiv  Detail & Related papers  (2024-05-02T20:42:28Z)
• Humans or LLMs as the Judge? A Study on Judgement Biases [17.069314000437537]
  We propose a novel framework that is free from referencing groundtruth annotations for investigating Misinformation Oversight Bias, Gender Bias, Authority Bias and Beauty Bias on LLM and human judges. Results show that human and LLM judges are vulnerable to perturbations to various degrees, and that even the cutting-edge judges possess considerable biases.
  arXiv  Detail & Related papers  (2024-02-16T13:21:06Z)
• Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs [8.526956860672698]
  Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities. This study investigates the potential of LLMs as reliable assessors of factual consistency in summaries generated by text-generation models.
  arXiv  Detail & Related papers  (2023-11-01T17:42:45Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• 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)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)
• 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.