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When Derived Measurements Mislead: Quantifying and... | AI Research

Key Takeaways

  • When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence addresses the tendency of large la...
  • 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.
  • ECG supplies training supervision and offline reference construction but is never shown to the LLM.
  • The framework does not depend on a particular reliability generator.
Paper AbstractExpand

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 this https URL .

When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence addresses the tendency of large language models (LLMs) to treat uncertain, upstream-derived data as absolute facts. This failure, termed derived-feature over-trust (DFOT), occurs when an LLM uses measurements—such as heart rhythm assessments from wearable devices—without accounting for whether those measurements are valid for a specific instance.

Defining the Problem

The authors, Zongheng Guo et al., identify that modern AI pipelines often pass structured data from specialized upstream models to a downstream LLM. Because the LLM typically lacks access to the original raw data or the acquisition conditions, it may assign an epistemic status of "direct fact" to a derived estimate. This differs from standard hallucinations or prediction errors because the model may reason coherently, yet still reach an incorrect conclusion by relying on a measurement that is inappropriate for the current context.

The Evaluation Framework

To quantify this behavior, the researchers developed a framework using physiological sensing as a case study. They utilized synchronized photoplethysmography (PPG) and electrocardiogram (ECG) data. While the ECG serves as a high-fidelity reference for training and evaluation, it is never shown to the LLM. The framework uses five specific metrics to measure the chain of trust:

  • COTR (Conflict Over-trust Rate): Measures how often the LLM accepts a derived measurement that contradicts the reference.

  • CIR (Context-Induced Error Rate): Measures how often misleading history causes the LLM to reject a valid measurement.

  • CRR (Correct Repair Rate): Measures the model's ability to correct its decision when provided with reliability evidence.

  • ESRM (Evidence-Specific Repair Margin): Contrasts matched evidence with patient-disjoint shuffled evidence to determine if the model is truly using instance-specific information.

  • UHR (Utility Harm Rate): Measures how often the model performs unnecessary verification on measurements that were already reliable.

Experimental Results

The researchers tested their approach on 50,000 paired PPG-ECG records. By using ECG-to-PPG privileged distillation, they created a baseline to compare how different reliability signals influence LLM behavior. On a test set of 187 patients, this method improved repair and specificity endpoints by 1.82 to 6.69 percentage points. The study noted that while the intervention improved performance, the Utility Harm Rate increased by 0.67 percentage points, indicating a trade-off between correcting errors and triggering unnecessary verification.

Limitations and Considerations

The authors emphasize that their framework is agnostic to the specific reliability generator used, meaning it can be applied to various uncertainty models. However, they clarify that their metrics characterize the inducibility of DFOT and the effectiveness of conditional repair, rather than estimating population-wide event frequencies. The study serves as a proof-of-concept for identifying and mitigating interface-level failures, providing a reproducible benchmark for future research into how LLMs handle uncertain, instance-dependent information.

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