@inproceedings{tan-etal-2025-consistent,
title = "Too Consistent to Detect: A Study of Self-Consistent Errors in {LLM}s",
author = "Tan, Hexiang and
Sun, Fei and
Liu, Sha and
Su, Du and
Cao, Qi and
Chen, Xin and
Wang, Jingang and
Cai, Xunliang and
Wang, Yuanzhuo and
Shen, Huawei and
Cheng, Xueqi",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.238/",
pages = "4755--4765",
ISBN = "979-8-89176-332-6",
abstract = "As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness.However, existing detection methods often overlook a critical problem we term as **self-consistent error**, where LLMs repeatedly generate the same incorrect response across multiple stochastic samples.This work formally defines self-consistent errors and evaluates mainstream detection methods on them.Our investigation reveals two key findings: (1) Unlike inconsistent errors, whose frequency diminishes significantly as the LLM scale increases, the frequency of self-consistent errors remains stable or even increases.(2) All four types of detection methods significantly struggle to detect self-consistent errors.These findings reveal critical limitations in current detection methods and underscore the need for improvement.Motivated by the observation that self-consistent errors often differ across LLMs, we propose a simple but effective \textit{cross{-}model probe} method that fuses hidden state evidence from an external verifier LLM.Our method significantly enhances performance on self-consistent errors across three LLM families."
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<abstract>As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness.However, existing detection methods often overlook a critical problem we term as **self-consistent error**, where LLMs repeatedly generate the same incorrect response across multiple stochastic samples.This work formally defines self-consistent errors and evaluates mainstream detection methods on them.Our investigation reveals two key findings: (1) Unlike inconsistent errors, whose frequency diminishes significantly as the LLM scale increases, the frequency of self-consistent errors remains stable or even increases.(2) All four types of detection methods significantly struggle to detect self-consistent errors.These findings reveal critical limitations in current detection methods and underscore the need for improvement.Motivated by the observation that self-consistent errors often differ across LLMs, we propose a simple but effective cross-model probe method that fuses hidden state evidence from an external verifier LLM.Our method significantly enhances performance on self-consistent errors across three LLM families.</abstract>
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%0 Conference Proceedings
%T Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs
%A Tan, Hexiang
%A Sun, Fei
%A Liu, Sha
%A Su, Du
%A Cao, Qi
%A Chen, Xin
%A Wang, Jingang
%A Cai, Xunliang
%A Wang, Yuanzhuo
%A Shen, Huawei
%A Cheng, Xueqi
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F tan-etal-2025-consistent
%X As large language models (LLMs) often generate plausible but incorrect content, error detection has become increasingly critical to ensure truthfulness.However, existing detection methods often overlook a critical problem we term as **self-consistent error**, where LLMs repeatedly generate the same incorrect response across multiple stochastic samples.This work formally defines self-consistent errors and evaluates mainstream detection methods on them.Our investigation reveals two key findings: (1) Unlike inconsistent errors, whose frequency diminishes significantly as the LLM scale increases, the frequency of self-consistent errors remains stable or even increases.(2) All four types of detection methods significantly struggle to detect self-consistent errors.These findings reveal critical limitations in current detection methods and underscore the need for improvement.Motivated by the observation that self-consistent errors often differ across LLMs, we propose a simple but effective cross-model probe method that fuses hidden state evidence from an external verifier LLM.Our method significantly enhances performance on self-consistent errors across three LLM families.
%U https://aclanthology.org/2025.emnlp-main.238/
%P 4755-4765
Markdown (Informal)
[Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs](https://aclanthology.org/2025.emnlp-main.238/) (Tan et al., EMNLP 2025)
ACL
- Hexiang Tan, Fei Sun, Sha Liu, Du Su, Qi Cao, Xin Chen, Jingang Wang, Xunliang Cai, Yuanzhuo Wang, Huawei Shen, and Xueqi Cheng. 2025. Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 4755–4765, Suzhou, China. Association for Computational Linguistics.