@inproceedings{si-etal-2025-aligning,
title = "Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering",
author = "Si, Shuzheng and
Zhao, Haozhe and
Chen, Gang and
Gao, Cheng and
Bai, Yuzhuo and
Wang, Zhitong and
An, Kaikai and
Luo, Kangyang and
Qian, Chen and
Qi, Fanchao and
Chang, Baobao and
Sun, Maosong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.804/",
doi = "10.18653/v1/2025.acl-long.804",
pages = "16469--16488",
ISBN = "979-8-89176-251-0",
abstract = "Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations. To address this challenge, we introduce NOVA, a novel framework designed to identify high-quality data that aligns well with the LLM{'}s learned knowledge to reduce hallucinations. NOVA includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data. Specifically, ICP evaluates the LLM{'}s understanding of the given instruction by calculating the tailored consistency among multiple self-generated responses. SEI further assesses the familiarity of the LLM with the target response by comparing it to the generated responses, using the proposed semantic clustering and well-designed voting strategy. Finally, to ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity. By considering data quality and avoiding unfamiliar data, we can utilize the selected data to effectively align LLMs to follow instructions and hallucinate less. Experiments show that NOVA significantly reduces hallucinations while maintaining a competitive ability to follow instructions."
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<abstract>Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations. To address this challenge, we introduce NOVA, a novel framework designed to identify high-quality data that aligns well with the LLM’s learned knowledge to reduce hallucinations. NOVA includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data. Specifically, ICP evaluates the LLM’s understanding of the given instruction by calculating the tailored consistency among multiple self-generated responses. SEI further assesses the familiarity of the LLM with the target response by comparing it to the generated responses, using the proposed semantic clustering and well-designed voting strategy. Finally, to ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity. By considering data quality and avoiding unfamiliar data, we can utilize the selected data to effectively align LLMs to follow instructions and hallucinate less. Experiments show that NOVA significantly reduces hallucinations while maintaining a competitive ability to follow instructions.</abstract>
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%0 Conference Proceedings
%T Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering
%A Si, Shuzheng
%A Zhao, Haozhe
%A Chen, Gang
%A Gao, Cheng
%A Bai, Yuzhuo
%A Wang, Zhitong
%A An, Kaikai
%A Luo, Kangyang
%A Qian, Chen
%A Qi, Fanchao
%A Chang, Baobao
%A Sun, Maosong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F si-etal-2025-aligning
%X Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations. To address this challenge, we introduce NOVA, a novel framework designed to identify high-quality data that aligns well with the LLM’s learned knowledge to reduce hallucinations. NOVA includes Internal Consistency Probing (ICP) and Semantic Equivalence Identification (SEI) to measure how familiar the LLM is with instruction data. Specifically, ICP evaluates the LLM’s understanding of the given instruction by calculating the tailored consistency among multiple self-generated responses. SEI further assesses the familiarity of the LLM with the target response by comparing it to the generated responses, using the proposed semantic clustering and well-designed voting strategy. Finally, to ensure the quality of selected samples, we introduce an expert-aligned reward model, considering characteristics beyond just familiarity. By considering data quality and avoiding unfamiliar data, we can utilize the selected data to effectively align LLMs to follow instructions and hallucinate less. Experiments show that NOVA significantly reduces hallucinations while maintaining a competitive ability to follow instructions.
%R 10.18653/v1/2025.acl-long.804
%U https://aclanthology.org/2025.acl-long.804/
%U https://doi.org/10.18653/v1/2025.acl-long.804
%P 16469-16488
Markdown (Informal)
[Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering](https://aclanthology.org/2025.acl-long.804/) (Si et al., ACL 2025)
ACL
- Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, and Maosong Sun. 2025. Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16469–16488, Vienna, Austria. Association for Computational Linguistics.