[2605.03196] Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability
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Computer Science > Computation and Language
arXiv:2605.03196 (cs)
[Submitted on 4 May 2026]
Title:Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability
Authors:Yucheng Du
View a PDF of the paper titled Geometric Deviation as an Unsupervised Pre-Generation Reliability Signal: Probing LLM Representations for Answerability, by Yucheng Du
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Abstract:A reliable language model should be able to signal, prior to generation, when a query falls outside its knowledge. We investigate whether representation geometry can provide such a pre-generation signal by measuring the deviation of hidden states from an answerable reference set, requiring no labeled failure data and no access to model outputs.
Across three instruction-tuned models (Llama 3.1-8B, Qwen 2.5-7B, and Mistral-7B-Instruct) and three prompt forms (Math, Fact, Code), we find that geometry primarily encodes task form. Within mathematical prompts, unanswerable inputs consistently deviate from the answerable centroid, yielding strong separation (ROC-AUC 0.78-0.84). This single-pass pre-generation signal outperforms a simple refusal baseline and compares favorably to self-consistency. It also captures cases where models do not explicitly refuse.
In contrast, no reliable geometric signal emerges for factual prompts, indicating that the effect is form-conditional rather than universal. Code prompts show large effect sizes with higher variance, suggesting partial generalization beyond mathematical form.
A layer-wise analysis reveals that the signal arises in early layers and gradually attenuates toward the output. These results suggest that answerability-related geometry is established before the final stages of generation. Together, these findings indicate that geometric deviation can serve as a lightweight pre-generation signal that is reliable in structured domains with formal answerability constraints, with clear boundaries on where it generalizes.
Comments: Accepted to TrustNLP 2026 (ACL Workshop). 11 pages, 3 figures, 3 tables
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
ACM classes: I.2.7
Cite as: arXiv:2605.03196 [cs.CL]
(or arXiv:2605.03196v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.03196
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yucheng Du [view email] [v1] Mon, 4 May 2026 22:24:34 UTC (746 KB)
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