When an AI pulling facts from medical records is confidently wrong, is that overconfidence the real reason it fails?
Does LLM miscalibration cause failures in clinical information extraction?
This explores whether models being confidently wrong, and not just wrong, is what breaks tasks like pulling structured facts out of clinical text, and what the corpus can and can't say about that.
This explores whether overconfidence (a model sounding sure when it shouldn't) is what makes LLMs fail at clinical information extraction. The corpus has one note that speaks directly to clinical text, and it points toward yes. On clinical natural language inference, models pair low accuracy with high confidence, and prompting techniques that improved general performance did not reduce the overconfidence Why do language models fail confidently in specialized domains?. The proposed cause is thin exposure. General-purpose training gives a model little practice with specialized material, and no internal signal that it is out of its depth. That note covers clinical inference, though, not extraction itself. Nothing in the corpus isolates miscalibration as the cause of extraction errors, so the rest of this answer rests on adjacent evidence.
Overconfidence matters because it makes failures silent. A study of document editing found that weaker models delete content visibly, while frontier models corrupt it in ways that keep the surface looking intact Does model capability change how documents degrade?. In a clinical record, a dropped medication gets noticed. A subtly altered dose or date doesn't. A related result comes from numerical optimization, not medicine. Models pattern-match to a familiar template and emit plausible-looking but wrong values instead of computing them Do large language models actually perform iterative optimization?. Applied to a lab value or a dose, that is a fluent number with nothing signaling that it's wrong.
Miscalibration also covers not noticing what is unclear in the input. On the AMBIENT benchmark, GPT-4 correctly disambiguates only 32% of ambiguous cases, against 90% for humans Can language models recognize when text is deliberately ambiguous?. Clinical notes are full of shorthand and hedged wording, so this gap is directly relevant. Standard benchmarks also filter out examples where annotators disagree, which removes exactly the cases that would expose the gap Do standard NLP benchmarks hide LLM ambiguity failures?. A model can look well calibrated on a benchmark and still fail on messy real notes. Humans would ask a clarifying question when a note is unclear, but current systems mostly answer without any repair loop Why do language models skip the calibration step?. A separate failure is that models often accept a false premise in the prompt instead of correcting it, because training rewarded agreement Why do language models agree with false claims they know are wrong?. That is a different problem from overconfidence and needs a different fix, so better calibration alone would not cover it.
The corpus also suggests two reasons the usual safeguards may not help. First, having another LLM check the extraction is risky, because LLM judges are swayed by fake authority signals and polished formatting regardless of content Can LLM judges be fooled by fake credentials and formatting?. Confident, well-formatted output is what a miscalibrated extractor produces. Second, asking a model to explain its extraction rule is weak reassurance. Models can explain a concept correctly, fail to apply it, and even recognize the failure afterward, which suggests the explaining and doing pathways are largely disconnected Can LLMs understand concepts they cannot apply?. One more lead is a framework that predicts failures where the correct answer is low-probability, even for logically simple tasks the-computational-level-predicts-where-llms-fail-embers-of-autoregression-anticipates. Rare or unusual clinical findings fit that description. That is my inference, since the framework wasn't tested on clinical data.
Overall, the corpus makes miscalibration a well-motivated suspect in clinical extraction failures, and direct evidence exists only for clinical inference. It also shows that overconfidence is one of several silent-failure mechanisms: undetected ambiguity, agreeable acceptance of false premises, and unreliable checking. Reliable clinical extraction probably needs more than better confidence scores.
Sources 10 notes
LLMs trained on general text lack sufficient exposure to domain-specific examples, leading to low accuracy paired with high confidence in clinical NLI tasks. Prompting techniques that improved general performance fail to reduce overconfidence in specialized domains.
DELEGATE-52 shows weaker LLMs degrade documents through visible deletion, while frontier models degrade through subtle corruption that preserves surface integrity. This shift makes frontier failures harder to detect and potentially more dangerous at workflow scale.
Research shows LLMs cannot perform iterative procedures in latent space. They recognize optimization problems as template-similar and emit plausible-looking but incorrect values, a failure mode that persists across model scale and training approaches.
AMBIENT benchmark shows GPT-4 correctly disambiguates only 32% of cases versus 90% for humans. This failure spans lexical, structural, and scope ambiguity—revealing that LLMs cannot hold multiple interpretations simultaneously, a fundamental gap hidden by standard benchmarks.
By filtering out examples where annotators disagree, benchmarks remove test cases that would reveal LLM failures at ambiguity recognition. Research using ambiguous examples shows a 32% vs. 90% accuracy gap invisible to standard evaluation.
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LLMs operate in static grounding mode—retrieving data and responding without clarification loops. Dynamic grounding, which humans use and which requires iterative repair, is largely absent from current systems, creating silent failures when intent diverges.
The FLEX benchmark shows models reject false presuppositions at dramatically different rates (GPT 84% vs Mistral 2.44%), not from ignorance but from preference for agreement learned via RLHF. This social accommodation is distinct from hallucination and requires different fixes.
Research identified four evaluation biases in LLM judges, with authority and beauty biases being semantics-agnostic and trivially exploitable through fake references and formatting—zero-shot attacks requiring no model access or optimization.
Models can explain concepts accurately, fail to apply them, and recognize the failure—a triple pattern incompatible with human cognition. This indicates functionally disconnected explanation and execution pathways rather than simple knowledge gaps.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey
- The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning
- Six misconceptions about large language models: A minimal model and diagnostic taxonomy
- Can LLMs Ground when they (Don't) Know: A Study on Direct and Loaded Political Questions
- Word Meanings in Transformer Language Models
- We’re Afraid Language Models Aren’t Modeling Ambiguity
- Large Language Model Reasoning Failures
- Intent Mismatch Causes LLMs to Get Lost in Multi-Turn Conversation