@inproceedings{yang-etal-2026-trilens,
title = "{T}ri{L}ens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection",
author = "Yang, Bohan and
Gong, Yijun and
Zhang, Zhi and
Zhang, Ge and
Xing, Wenpeng and
Han, Meng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1803/",
pages = "38536--38553",
ISBN = "979-8-89176-462-0",
abstract = "When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathways may remain uncertain, disagree in how quickly they sharpen, or commit to competing continuations before the output is produced. We introduce \textit{TriLens}, a white-box detector that turns this intuition into a compact representation: at every layer, it reads the multi-head self-attention output, the feed-forward output, and the residual stream through the model{'}s own logit lens, then records only the entropy of each readout. The resulting $3L$-dimensional trajectory describes how certainty forms across depth and across modules, without storing high-dimensional hidden states or sampling multiple generations. This simple signal yields a strong detector across instruction-tuned LLMs and QA benchmarks, and our analyses show that the three module-wise entropy trajectories provide complementary evidence. TriLens suggests that hallucination detection can benefit from tracking how internal computation settles, not only what the final layer predicts. The code is available at https://tosakaucw.github.io/TriLens/."
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<abstract>When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathways may remain uncertain, disagree in how quickly they sharpen, or commit to competing continuations before the output is produced. We introduce TriLens, a white-box detector that turns this intuition into a compact representation: at every layer, it reads the multi-head self-attention output, the feed-forward output, and the residual stream through the model’s own logit lens, then records only the entropy of each readout. The resulting 3L-dimensional trajectory describes how certainty forms across depth and across modules, without storing high-dimensional hidden states or sampling multiple generations. This simple signal yields a strong detector across instruction-tuned LLMs and QA benchmarks, and our analyses show that the three module-wise entropy trajectories provide complementary evidence. TriLens suggests that hallucination detection can benefit from tracking how internal computation settles, not only what the final layer predicts. The code is available at https://tosakaucw.github.io/TriLens/.</abstract>
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%0 Conference Proceedings
%T TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection
%A Yang, Bohan
%A Gong, Yijun
%A Zhang, Zhi
%A Zhang, Ge
%A Xing, Wenpeng
%A Han, Meng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F yang-etal-2026-trilens
%X When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathways may remain uncertain, disagree in how quickly they sharpen, or commit to competing continuations before the output is produced. We introduce TriLens, a white-box detector that turns this intuition into a compact representation: at every layer, it reads the multi-head self-attention output, the feed-forward output, and the residual stream through the model’s own logit lens, then records only the entropy of each readout. The resulting 3L-dimensional trajectory describes how certainty forms across depth and across modules, without storing high-dimensional hidden states or sampling multiple generations. This simple signal yields a strong detector across instruction-tuned LLMs and QA benchmarks, and our analyses show that the three module-wise entropy trajectories provide complementary evidence. TriLens suggests that hallucination detection can benefit from tracking how internal computation settles, not only what the final layer predicts. The code is available at https://tosakaucw.github.io/TriLens/.
%U https://aclanthology.org/2026.findings-emnlp.1803/
%P 38536-38553
Markdown (Informal)
[TriLens: Per-Layer Logit-Lens Entropy for White-Box Hallucination Detection](https://aclanthology.org/2026.findings-emnlp.1803/) (Yang et al., Findings 2026)
ACL