Enhancing Hallucination Detection via Future Context

Joosung Lee; Cheonbok Park; Hwiyeol Jo; Jeonghoon Kim; Joonsuk Park; Kang Min Yoo

Enhancing Hallucination Detection via Future Context

Joosung Lee, Cheonbok Park, Hwiyeol Jo, Jeonghoon Kim, Joonsuk Park, Kang Min Yoo

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Abstract

Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process.As users increasingly encounter such black-box outputs, detecting hallucinations has become a critical challenge.To address this challenge, we focus on developing a hallucination detection framework for black-box generators.Motivated by the observation that hallucinations, once introduced, tend to persist, we sample future contexts.The sampled future contexts provide valuable clues for hallucination detection and can be effectively integrated with various sampling-based methods.We extensively demonstrate performance improvements across multiple methods using our proposed sampling approach.

Anthology ID:: 2026.findings-acl.35
Volume:: Findings of the Association for Computational Linguistics: ACL 2026
Month:: July
Year:: 2026
Address:: San Diego, California, United States
Editors:: Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:: Findings
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 731–749
Language:
URL:: https://aclanthology.org/2026.findings-acl.35/
DOI:
Bibkey:
Cite (ACL):: Joosung Lee, Cheonbok Park, Hwiyeol Jo, Jeonghoon Kim, Joonsuk Park, and Kang Min Yoo. 2026. Enhancing Hallucination Detection via Future Context. In Findings of the Association for Computational Linguistics: ACL 2026, pages 731–749, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):: Enhancing Hallucination Detection via Future Context (Lee et al., Findings 2026)
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PDF:: https://aclanthology.org/2026.findings-acl.35.pdf
Checklist:: 2026.findings-acl.35.checklist.pdf

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@inproceedings{lee-etal-2026-enhancing,
    title = "Enhancing Hallucination Detection via Future Context",
    author = "Lee, Joosung  and
      Park, Cheonbok  and
      Jo, Hwiyeol  and
      Kim, Jeonghoon  and
      Park, Joonsuk  and
      Yoo, Kang Min",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-acl.35/",
    pages = "731--749",
    ISBN = "979-8-89176-395-1",
    abstract = "Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process.As users increasingly encounter such black-box outputs, detecting hallucinations has become a critical challenge.To address this challenge, we focus on developing a hallucination detection framework for black-box generators.Motivated by the observation that hallucinations, once introduced, tend to persist, we sample future contexts.The sampled future contexts provide valuable clues for hallucination detection and can be effectively integrated with various sampling-based methods.We extensively demonstrate performance improvements across multiple methods using our proposed sampling approach."
}

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%0 Conference Proceedings
%T Enhancing Hallucination Detection via Future Context
%A Lee, Joosung
%A Park, Cheonbok
%A Jo, Hwiyeol
%A Kim, Jeonghoon
%A Park, Joonsuk
%A Yoo, Kang Min
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lee-etal-2026-enhancing
%X Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process.As users increasingly encounter such black-box outputs, detecting hallucinations has become a critical challenge.To address this challenge, we focus on developing a hallucination detection framework for black-box generators.Motivated by the observation that hallucinations, once introduced, tend to persist, we sample future contexts.The sampled future contexts provide valuable clues for hallucination detection and can be effectively integrated with various sampling-based methods.We extensively demonstrate performance improvements across multiple methods using our proposed sampling approach.
%U https://aclanthology.org/2026.findings-acl.35/
%P 731-749

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Markdown (Informal)

[Enhancing Hallucination Detection via Future Context](https://aclanthology.org/2026.findings-acl.35/) (Lee et al., Findings 2026)

Enhancing Hallucination Detection via Future Context (Lee et al., Findings 2026)

ACL

Joosung Lee, Cheonbok Park, Hwiyeol Jo, Jeonghoon Kim, Joonsuk Park, and Kang Min Yoo. 2026. Enhancing Hallucination Detection via Future Context. In Findings of the Association for Computational Linguistics: ACL 2026, pages 731–749, San Diego, California, United States. Association for Computational Linguistics.