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Source note3 min read

When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence

Source

arXiv

arxiv.org/abs/2607.28421v1

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Plain-Language Brief

The study presents a framework to quantify and address Derived-Feature Over-Trust (DFOT), which occurs when large language models (LLMs) misuse derived measurements as if they were direct facts. The researchers use physiological sensing as a case study, testing the acceptance of a PPG-derived rhythm contradicted by offline ECG and the rejection of a reliable PPG rhythm under misleading severe history. The framework includes five estimators to assess this chain, with the results showing improvement in four repair and specificity endpoints when using ECG-to-PPG privileged distillation as a baseline.

What the Document Says

  • The study defines Derived-Feature Over-Trust (DFOT) as the failure of a downstream LLM to correctly use or interpret derived measurements within their valid scope.
  • Physiological sensing is used as a case study to test the acceptance of a PPG-derived rhythm contradicted by offline ECG and the rejection of a reliable PPG rhythm under misleading severe history.
  • ECG supplies training supervision and offline reference construction but is never shown to the LLM.
  • Five estimators are used: conflict over-trust rate (COTR), context-induced error rate (CIR), correct repair rate (CRR), evidence-specific repair margin (ESRM), and utility harm rate (UHR).
  • The framework is independent of a particular reliability generator.
  • The study demonstrates the framework using 50,000 paired PPG-ECG records, with ECG-to-PPG privileged distillation as an illustrative baseline and PPG-only inference.
  • On a protocol-locked 187-patient test, the baseline improves four repair and specificity endpoints by 1.82-6.69 percentage points, with all paired confidence intervals excluding zero; UHR increases by 0.67 percentage points (95% CI: -0.4 to +1.7).
  • The study's code is available at https://github.com/Zongheng-Guo/When-Derived-Measurements-Mislead.

Key Concepts Mentioned

  • Derived-Feature Over-Trust (DFOT)
  • large language models (LLMs)
  • physiological sensing
  • ECG
  • PPG-derived rhythm
  • offline ECG
  • privileged distillation

Claims Made by the Document

  • DFOT occurs when downstream LLMs misuse derived measurements.
  • The proposed framework can quantify DFOT and provide a common evaluation target for stronger mitigation methods.

Evidence Used

  • The study defines DFOT and outlines the framework to address it.
  • The results of the study demonstrate improvement in repair and specificity endpoints when using ECG-to-PPG privileged distillation as a baseline.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Derived measurements increasingly enter large language model (LLM) pipelines as direct facts despite their instance-dependent validity. We define derived-feature over-trust (DFOT) as the failure in which a downstream LLM assigns such a measurement the epistemic status of a direct fact or uses it outside its valid scope. Using physiological sensing as a case study, D1 tests acceptance of a PPG-derived rhythm contradicted by offline ECG, whereas D2 tests rejection of an offline-confirmed reliable PPG rhythm under misleading severe history. ECG supplies training supervision and offline reference construction but is never shown to the LLM. Five estimands quantify this chain: conflict over-trust rate (COTR) and context-induced error rate (CIR) characterize D1/D2; correct repair rate (CRR) measures frozen-error repair; evidence-specific repair margin (ESRM) contrasts matched and patient-disjoint shuffled evidence; and utility harm rate (UHR) measures unnecessary verification among HIGH-reliability cases used without verification at baseline. The framework does not depend on a particular reliability generator. We demonstrate it on 50,000 paired PPG-ECG records using ECG-to-PPG privileged distillation as an illustrative baseline and PPG-only inference. On a protocol-locked 187-patient test, the baseline improves four repair and specificity endpoints by 1.82-6.69 percentage points, with all paired confidence intervals excluding zero; UHR increases by 0.67 percentage points (95% CI: -0.4 to +1.7). DFOT provides a common evaluation target for stronger mitigation methods. The code is available at https://github.com/Zongheng-Guo/When-Derived-Measurements-Mislead.