@inproceedings{rao-etal-2025-apt,
title = "{APT}: Improving Specialist {LLM} Performance with Weakness Case Acquisition and Iterative Preference Training",
author = "Rao, Jun and
Lin, Zepeng and
Liu, Xuebo and
Ke, Xiaopeng and
Lian, Lian and
Jin, Dong and
Cheng, Shengjun and
Yu, Jun and
Zhang, Min",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1079/",
doi = "10.18653/v1/2025.findings-acl.1079",
pages = "20958--20980",
ISBN = "979-8-89176-256-5",
abstract = "Large Language Models (LLMs) often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. Maintaining a balance between domain-specific enhancements and general model utility is a key challenge. This paper proposes a novel approach named APT (Weakness Case Acquisition and Iterative Preference Training) to enhance domain-specific performance with self-generated dis-preferred weakness data (bad cases and similar cases). APT uniquely focuses on training the model using only those samples where errors occur, alongside a small, similar set of samples retrieved for this purpose. This targeted training minimizes interference with the model{'}s existing knowledge base, effectively retaining generic capabilities. Experimental results on the LLama-2 and Mistral-V0.3 models across various benchmarks demonstrate that APT ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to various existing methods. This validates our method as an effective strategy for enhancing domain-specific capabilities without sacrificing the model{'}s broader applicability."
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<abstract>Large Language Models (LLMs) often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. Maintaining a balance between domain-specific enhancements and general model utility is a key challenge. This paper proposes a novel approach named APT (Weakness Case Acquisition and Iterative Preference Training) to enhance domain-specific performance with self-generated dis-preferred weakness data (bad cases and similar cases). APT uniquely focuses on training the model using only those samples where errors occur, alongside a small, similar set of samples retrieved for this purpose. This targeted training minimizes interference with the model’s existing knowledge base, effectively retaining generic capabilities. Experimental results on the LLama-2 and Mistral-V0.3 models across various benchmarks demonstrate that APT ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to various existing methods. This validates our method as an effective strategy for enhancing domain-specific capabilities without sacrificing the model’s broader applicability.</abstract>
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%0 Conference Proceedings
%T APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training
%A Rao, Jun
%A Lin, Zepeng
%A Liu, Xuebo
%A Ke, Xiaopeng
%A Lian, Lian
%A Jin, Dong
%A Cheng, Shengjun
%A Yu, Jun
%A Zhang, Min
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F rao-etal-2025-apt
%X Large Language Models (LLMs) often require domain-specific fine-tuning to address targeted tasks, which risks degrading their general capabilities. Maintaining a balance between domain-specific enhancements and general model utility is a key challenge. This paper proposes a novel approach named APT (Weakness Case Acquisition and Iterative Preference Training) to enhance domain-specific performance with self-generated dis-preferred weakness data (bad cases and similar cases). APT uniquely focuses on training the model using only those samples where errors occur, alongside a small, similar set of samples retrieved for this purpose. This targeted training minimizes interference with the model’s existing knowledge base, effectively retaining generic capabilities. Experimental results on the LLama-2 and Mistral-V0.3 models across various benchmarks demonstrate that APT ensures no reduction in generic capacity and achieves superior performance on downstream tasks compared to various existing methods. This validates our method as an effective strategy for enhancing domain-specific capabilities without sacrificing the model’s broader applicability.
%R 10.18653/v1/2025.findings-acl.1079
%U https://aclanthology.org/2025.findings-acl.1079/
%U https://doi.org/10.18653/v1/2025.findings-acl.1079
%P 20958-20980
Markdown (Informal)
[APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training](https://aclanthology.org/2025.findings-acl.1079/) (Rao et al., Findings 2025)
ACL
- Jun Rao, Zepeng Lin, Xuebo Liu, Xiaopeng Ke, Lian Lian, Dong Jin, Shengjun Cheng, Jun Yu, and Min Zhang. 2025. APT: Improving Specialist LLM Performance with Weakness Case Acquisition and Iterative Preference Training. In Findings of the Association for Computational Linguistics: ACL 2025, pages 20958–20980, Vienna, Austria. Association for Computational Linguistics.