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

Title: LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks
Authors: Bavaresco, Anna; Bernardi, Raffaella; Bertolazzi, Leonardo; Elliott, Desmond; Fernández, Raquel; Gatt, Albert; Ghaleb, Esam; Giulianelli, Mario; Hanna, Michael; Koller, Alexander; Martins, André F.T.; Mondorf, Philipp; Neplenbroek, Vera; Pezzelle, Sandro; Plank, Barbara; Schlangen, David; Suglia, Alessandro; Surikuchi, Aditya K.; Takmaz, Ece; Testoni, Alberto; Sub Natural Language Processing; LS Comp.semantiek en kunstm.intelligent.; Che, Wanxiang; Nabende, Joyce; Shutova, Ekaterina; Pilehvar, Mohammad Taher
Publication Year: 2025
Subject Terms: Language and Linguistics; Linguistics and Language; Computer Science Applications
Description: There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reproducibility in the case of proprietary models. We provide JUDGE-BENCH, an extensible collection of 20 NLP datasets with human annotations covering a broad range of evaluated properties and types of data, and comprehensively evaluate 11 current LLMs, covering both open-weight and proprietary models, for their ability to replicate the annotations. Our evaluations show substantial variance across models and datasets. Models are reliable evaluators on some tasks, but overall display substantial variability depending on the property being evaluated, the expertise level of the human judges, and whether the language is human or model-generated. We conclude that LLMs should be carefully validated against human judgments before being used as evaluators.
Document Type: book part
File Description: application/pdf
Language: English
ISSN: 0736-587X
Relation: https://dspace.library.uu.nl/handle/1874/483207
Availability: https://dspace.library.uu.nl/handle/1874/483207
Rights: info:eu-repo/semantics/OpenAccess
Accession Number: edsbas.866CC980
Database: BASE