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.- Anthology ID:
- 2024.findings-acl.799
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13462–13474
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.799
- DOI:
- 10.18653/v1/2024.findings-acl.799
- Cite (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.
- Cite (Informal):
- InstructEval: Instruction-Tuned Text Evaluator from Human Preference (Wu et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.799.pdf