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        <title>Know Your Limits: A Survey of Abstention in Large Language Models</title>
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    <name type="personal">
        <namePart type="given">Bingbing</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
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    <abstract>Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in LLM systems. In this survey, we introduce a framework to examine abstention from three perspectives: the query, the model, and human values. We organize the literature on abstention methods, benchmarks, and evaluation metrics using this framework, and discuss merits and limitations of prior work. We further identify and motivate areas for future research, such as whether abstention can be achieved as a meta-capability that transcends specific tasks or domains, and opportunities to optimize abstention abilities in specific contexts. In doing so, we aim to broaden the scope and impact of abstention methodologies in AI systems.1</abstract>
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    <identifier type="doi">10.1162/tacl_a_00754</identifier>
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        <date>2025</date>
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%0 Journal Article
%T Know Your Limits: A Survey of Abstention in Large Language Models
%A Wen, Bingbing
%A Yao, Jihan
%A Feng, Shangbin
%A Xu, Chenjun
%A Tsvetkov, Yulia
%A Howe, Bill
%A Wang, Lucy Lu
%J Transactions of the Association for Computational Linguistics
%D 2025
%V 13
%I MIT Press
%C Cambridge, MA
%F wen-etal-2025-know
%X Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in LLM systems. In this survey, we introduce a framework to examine abstention from three perspectives: the query, the model, and human values. We organize the literature on abstention methods, benchmarks, and evaluation metrics using this framework, and discuss merits and limitations of prior work. We further identify and motivate areas for future research, such as whether abstention can be achieved as a meta-capability that transcends specific tasks or domains, and opportunities to optimize abstention abilities in specific contexts. In doing so, we aim to broaden the scope and impact of abstention methodologies in AI systems.1
%R 10.1162/tacl_a_00754
%U https://aclanthology.org/2025.tacl-1.26/
%U https://doi.org/10.1162/tacl_a_00754
%P 529-556

Markdown (Informal)

[Know Your Limits: A Survey of Abstention in Large Language Models](https://aclanthology.org/2025.tacl-1.26/) (Wen et al., TACL 2025)

• Know Your Limits: A Survey of Abstention in Large Language Models (Wen et al., TACL 2025)

ACL

• Bingbing Wen, Jihan Yao, Shangbin Feng, Chenjun Xu, Yulia Tsvetkov, Bill Howe, and Lucy Lu Wang. 2025. Know Your Limits: A Survey of Abstention in Large Language Models. Transactions of the Association for Computational Linguistics, 13:529–556.