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    <abstract>This paper explores to construct a general text evaluator based on open-source Large Language Models (LLMs), a domain predominantly occupied by commercial counterparts such as GPT-4. Recognizing the limitations of open-source models like Llama in evaluative tasks, we introduce InstructEval, a general multi-aspect text evaluator developed through instruction tuning of open-source LLMs. To overcome the shortage of annotated resources for multi-aspect evaluations, InstructEval combines extensive open Human Preference Modeling (HPM) datasets with a small set of multi-aspect annotated data.This approach not only enhances effectiveness in overall evaluation tasks but also exhibits improved performance in multi-aspect evaluation tasks.As demonstrated by our extensive experiments, InstructEval achieves comparable or superior performance to commercial LLMs like ChatGPT or GPT-4 in terms of both overall and multi-aspect evaluation.</abstract>
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%0 Conference Proceedings
%T InstructEval: Instruction-Tuned Text Evaluator from Human Preference
%A Wu, Wenhao
%A Li, Wei
%A Xiao, Xinyan
%A Liu, Jiachen
%A Li, Sujian
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F wu-etal-2024-instructeval
%X This paper explores to construct a general text evaluator based on open-source Large Language Models (LLMs), a domain predominantly occupied by commercial counterparts such as GPT-4. Recognizing the limitations of open-source models like Llama in evaluative tasks, we introduce InstructEval, a general multi-aspect text evaluator developed through instruction tuning of open-source LLMs. To overcome the shortage of annotated resources for multi-aspect evaluations, InstructEval combines extensive open Human Preference Modeling (HPM) datasets with a small set of multi-aspect annotated data.This approach not only enhances effectiveness in overall evaluation tasks but also exhibits improved performance in multi-aspect evaluation tasks.As demonstrated by our extensive experiments, InstructEval achieves comparable or superior performance to commercial LLMs like ChatGPT or GPT-4 in terms of both overall and multi-aspect evaluation.
%R 10.18653/v1/2024.findings-acl.799
%U https://aclanthology.org/2024.findings-acl.799/
%U https://doi.org/10.18653/v1/2024.findings-acl.799
%P 13462-13474

Markdown (Informal)

[InstructEval: Instruction-Tuned Text Evaluator from Human Preference](https://aclanthology.org/2024.findings-acl.799/) (Wu et al., Findings 2024)

• InstructEval: Instruction-Tuned Text Evaluator from Human Preference (Wu et al., Findings 2024)

ACL

• Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, and Sujian Li. 2024. InstructEval: Instruction-Tuned Text Evaluator from Human Preference. In Findings of the Association for Computational Linguistics: ACL 2024, pages 13462–13474, Bangkok, Thailand. Association for Computational Linguistics.