@inproceedings{fu-etal-2026-anchoring,
title = "Anchoring the Cache: Mitigating Contextual Hallucination in {KV}-Compressed Long-Context Summarization",
author = "Fu, Yu and
Luo, Chen and
Valvoda, Josef and
Zhang, Xin and
Lei, Xuejing and
Pan, Xiao and
Liu, Hui and
Dong, Yue",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1542/",
pages = "33397--33413",
ISBN = "979-8-89176-390-6",
abstract = "Key-Value (KV) cache compression techniques have improved the efficiency of long-context summarization in Large Language Models (LLMs), but their impact on model hallucination remains underexplored. In this paper, we present the first systematic study of how KV cache compression affects hallucination in long-context summarization, demonstrating that aggressive compression can increase hallucination scores by up to 3.36{\texttimes} compared to the baseline. To mitigate this issue, we propose HalluKV, a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context, thereby anchoring their attention on the preserved source information. Our approach maintains computational efficiency while significantly reducing hallucination across multiple models and datasets, achieving up to 5.48 average point reductions on Llama-3-8B-Instruct, enabling more trustworthy long-context summarization."
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<abstract>Key-Value (KV) cache compression techniques have improved the efficiency of long-context summarization in Large Language Models (LLMs), but their impact on model hallucination remains underexplored. In this paper, we present the first systematic study of how KV cache compression affects hallucination in long-context summarization, demonstrating that aggressive compression can increase hallucination scores by up to 3.36× compared to the baseline. To mitigate this issue, we propose HalluKV, a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context, thereby anchoring their attention on the preserved source information. Our approach maintains computational efficiency while significantly reducing hallucination across multiple models and datasets, achieving up to 5.48 average point reductions on Llama-3-8B-Instruct, enabling more trustworthy long-context summarization.</abstract>
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%0 Conference Proceedings
%T Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization
%A Fu, Yu
%A Luo, Chen
%A Valvoda, Josef
%A Zhang, Xin
%A Lei, Xuejing
%A Pan, Xiao
%A Liu, Hui
%A Dong, Yue
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F fu-etal-2026-anchoring
%X Key-Value (KV) cache compression techniques have improved the efficiency of long-context summarization in Large Language Models (LLMs), but their impact on model hallucination remains underexplored. In this paper, we present the first systematic study of how KV cache compression affects hallucination in long-context summarization, demonstrating that aggressive compression can increase hallucination scores by up to 3.36× compared to the baseline. To mitigate this issue, we propose HalluKV, a decoding-phase strategy that selectively removes generated KV pairs from retrieval heads responsible for retrieving critical information from source context, thereby anchoring their attention on the preserved source information. Our approach maintains computational efficiency while significantly reducing hallucination across multiple models and datasets, achieving up to 5.48 average point reductions on Llama-3-8B-Instruct, enabling more trustworthy long-context summarization.
%U https://aclanthology.org/2026.acl-long.1542/
%P 33397-33413
Markdown (Informal)
[Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization](https://aclanthology.org/2026.acl-long.1542/) (Fu et al., ACL 2026)
ACL
- Yu Fu, Chen Luo, Josef Valvoda, Xin Zhang, Xuejing Lei, Xiao Pan, Hui Liu, and Yue Dong. 2026. Anchoring the Cache: Mitigating Contextual Hallucination in KV-Compressed Long-Context Summarization. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 33397–33413, San Diego, California, United States. Association for Computational Linguistics.