@inproceedings{ling-etal-2026-llm,
title = "{LLM} Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty",
author = "Ling, Zipeng and
Liu, Shuliang and
Tang, Yuehao and
Yang, Junqi and
Fu, Shenghong and
Son, Seonil and
Huang, Chen and
Huang, Kejia and
Wan, Yao and
Hou, Zhichao and
Hu, Xuming",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.549/",
pages = "12612--12633",
ISBN = "979-8-89176-459-0",
abstract = "Large Language Models (LLMs) are increasingly trained to abstain from answering questions they are unsure about. However, this ability is often misused: in real-world applications, input prompts sometimes contain uncertainty elements, and driven by this, LLMs are inclined to abstain even on problems they are capable of solving. We argue that LLM abstention is not only an expression of genuine uncertainty; it is also an artifact that can be largely influenced by prompts. We name this phenomenon *Abstention Inflation*. We add ``Unknown'' as an extra option for LLMs to choose from; experiments show serious accuracy drops on True/False Questions (TFQs). Replacing ``Unknown'' with an unrelated random word produces an identical effect. We argue that LLMs are trained to imitate the surface pattern of abstention, rather than to express genuine uncertainty. Based on ten experiments, we support four claims that form a progressive argument: **(C1)** *Abstention Inflation* is triggered by the structural presence of an extra option, not by genuine uncertainty; **(C2)** further, it makes the model deny it can answer even when it can; **(C3)** at the representation level, this manifests as a later-layer output override; **(C4)** finally, this bias is stable and emerges through instruction tuning, rather than stochastic noise."
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<abstract>Large Language Models (LLMs) are increasingly trained to abstain from answering questions they are unsure about. However, this ability is often misused: in real-world applications, input prompts sometimes contain uncertainty elements, and driven by this, LLMs are inclined to abstain even on problems they are capable of solving. We argue that LLM abstention is not only an expression of genuine uncertainty; it is also an artifact that can be largely influenced by prompts. We name this phenomenon *Abstention Inflation*. We add “Unknown” as an extra option for LLMs to choose from; experiments show serious accuracy drops on True/False Questions (TFQs). Replacing “Unknown” with an unrelated random word produces an identical effect. We argue that LLMs are trained to imitate the surface pattern of abstention, rather than to express genuine uncertainty. Based on ten experiments, we support four claims that form a progressive argument: **(C1)** *Abstention Inflation* is triggered by the structural presence of an extra option, not by genuine uncertainty; **(C2)** further, it makes the model deny it can answer even when it can; **(C3)** at the representation level, this manifests as a later-layer output override; **(C4)** finally, this bias is stable and emerges through instruction tuning, rather than stochastic noise.</abstract>
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%0 Conference Proceedings
%T LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty
%A Ling, Zipeng
%A Liu, Shuliang
%A Tang, Yuehao
%A Yang, Junqi
%A Fu, Shenghong
%A Son, Seonil
%A Huang, Chen
%A Huang, Kejia
%A Wan, Yao
%A Hou, Zhichao
%A Hu, Xuming
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ling-etal-2026-llm
%X Large Language Models (LLMs) are increasingly trained to abstain from answering questions they are unsure about. However, this ability is often misused: in real-world applications, input prompts sometimes contain uncertainty elements, and driven by this, LLMs are inclined to abstain even on problems they are capable of solving. We argue that LLM abstention is not only an expression of genuine uncertainty; it is also an artifact that can be largely influenced by prompts. We name this phenomenon *Abstention Inflation*. We add “Unknown” as an extra option for LLMs to choose from; experiments show serious accuracy drops on True/False Questions (TFQs). Replacing “Unknown” with an unrelated random word produces an identical effect. We argue that LLMs are trained to imitate the surface pattern of abstention, rather than to express genuine uncertainty. Based on ten experiments, we support four claims that form a progressive argument: **(C1)** *Abstention Inflation* is triggered by the structural presence of an extra option, not by genuine uncertainty; **(C2)** further, it makes the model deny it can answer even when it can; **(C3)** at the representation level, this manifests as a later-layer output override; **(C4)** finally, this bias is stable and emerges through instruction tuning, rather than stochastic noise.
%U https://aclanthology.org/2026.emnlp-main.549/
%P 12612-12633
Markdown (Informal)
[LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty](https://aclanthology.org/2026.emnlp-main.549/) (Ling et al., EMNLP 2026)
ACL
- Zipeng Ling, Shuliang Liu, Yuehao Tang, Junqi Yang, Shenghong Fu, Seonil Son, Chen Huang, Kejia Huang, Yao Wan, Zhichao Hou, and Xuming Hu. 2026. LLM Abstention Can Be a Prompt Artifact, in Addition to Genuine Uncertainty. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 12612–12633, Budapest, Hungary. Association for Computational Linguistics.