Confidently Wrong: Detecting Hallucinations in Financial Question Answering from LLM Internal States
Factual evidence
What the source reports
Research explores detecting confident hallucinations in LLMs for financial Q&A by analyzing internal model activations, beyond observable outputs.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 15 October 2026
- Collected by OneBench
- 14 Jul 2026, 09:33 UK
Stored source excerpt
arXiv:2607.11414v1 Announce Type: new Abstract: Large language models (LLMs) in financial applications fail most consequentially when they are confidently wrong. Hedged, uncertain answers invite scrutiny,…
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OneBench interpretation
Institutional assessment
So what
Reliably detecting confidently wrong LLM answers using internal states directly addresses a critical model risk that current external evaluation methods struggle with for G-SIBs.
Do what
This research provides a pathway for enhancing your LLM model validation and real-time risk monitoring frameworks, potentially improving trust in deployed financial AI applications.