LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data
Quick Answer
This study reveals that large language models (LLMs) like Qwen 2.5 7B struggle with epistemic self-awareness on clinical tabular data, showing constant confidence levels regardless of accuracy.
Quick Take
By employing cross-model attribution divergence, the research demonstrates that integrating few-shot examples and SHAP-derived features can significantly enhance prediction accuracy from 49% to 75.3% and reduce calibration error.
Key Points
- confidence remains constant (0.856-0.937) despite varying accuracy levels.
- Accuracy drops to 64.8% when XGBoost is 99% correct, indicating an inverse difficulty effect.
- Few-shot examples and SHAP features reduce Attribution Disagreement Score from 1.54 to 0.38.
- Cross-model calibrator improves expected calibration error from 0.254 to 0.080.
- Findings highlight a cold start problem for LLMs on structured data.
Paper Resources
Source Excerpt
(LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored. We study this question through the lens of cross-model attribution divergence with the goal of reducing epistemic uncertainty for structured tasks, comparing Qwen 2. 5 7B and XGBoost on a prediction task via attribution divergence analysis. We report four findings. First, LLM verbalized confidence is epistemically vac
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