LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios
Open accessAbstract
Xiaodong Wu, Minhao Wang, Yichen Liu, Xiaoming Shi, He Yan, Lu Xiangju, Junmin Zhu, Wei Zhang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
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Cites
Levy, M., Jacoby, A., & Goldberg, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.818 · Cited by 119 · see citing works
InFoBench: Evaluating Instruction Following Ability in Large Language Models
Qin, Y., Song, K., Hu, Y., Yao, W., Cho, S., Wang, X., Wu, X., Liu, F., Liu, P., & Yu, D. (2024) · Findings of the Association for Computational Linguistics ACL 2024 · doi:10.18653/v1/2024.findings-acl.772 · Cited by 77 · see citing works
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- Authors
- Wu, X., Wang, M., Liu, Y., Shi, X., Yan, H., Xiangju, L., Zhu, J., & Zhang, W.
- Affiliations
- East China Normal University (CN)
- iQIYI (China) (CN)
- Shanghai Innovation Institute (CN)
- Venue
- Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2025
- Pages
- 16445-16468
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4412945109
- Cited by
- 4
- References
- 2
- Citations by year
- 2026: 4
- FWCI
- 2.81
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (92270119)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, instruction following, model stability, benchmark evaluation
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