Why do language models fail confidently in specialized domains?
LLMs perform poorly on clinical and biomedical inference tasks while remaining overconfident in their wrong answers. Do standard benchmarks hide this fragility, and can prompting techniques fix it?
"Rethinking STS and NLI in Large Language Models" evaluates LLMs on clinical/biomedical NLI and semantic textual similarity — domains requiring expert annotation, yielding small datasets (<2,000 examples). Three persistent problems:
Low accuracy in low-resource knowledge-rich domains — exposure bias: LLMs are not exposed to sufficient domain-specific training examples, so their NLI/STS accuracy in clinical contexts is substantially lower than in general domains. General benchmark performance does not predict specialized domain performance.
Overconfidence — models make incorrect predictions over-confidently. This is dangerous in safety-critical applications: an LLM that is wrong and certain provides no useful signal for downstream decision support. Prompting LLMs, which showed dramatic improvement on general NLI tasks in the text-davinci era, does not solve overconfidence in specialized domains.
Difficulty capturing collective human opinion distributions — NLI annotation sometimes reflects genuine human disagreement, and the distribution of opinions carries meaning beyond the majority label. Bayesian estimation of LLM uncertainty is computationally prohibitive; persona-based approaches (instructing LLMs to simulate different annotator profiles) are unstable.
The implication: the widely noted improvement in LLM NLI performance on standard benchmarks masks persistent fragility on specialized, knowledge-rich domains. Since Do classical knowledge definitions apply to AI systems?, LLMs may appear to reason well without having the domain knowledge that grounds reliable specialized inference.
This is a domain-specificity limitation that is structurally different from general reasoning failure — it emerges specifically at the boundary where general-purpose pretraining meets specialized expert knowledge. The vocabulary, entity relationships, and inference patterns of clinical medicine are not proportionally represented in general pretraining corpora.
Source: Natural Language Inference
Related concepts in this collection
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Do classical knowledge definitions apply to AI systems?
Classical definitions of knowledge assume truth-correspondence and a human knower. Do these assumptions hold for LLMs and distributed neural knowledge systems, or do they need fundamental revision?
LLM "knowledge" in specialized domains is thin and unreliable even when performance appears adequate on general benchmarks
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Does LLM grammatical performance decline with structural complexity?
This explores whether LLMs fail uniformly at grammar or whether their failures follow a predictable pattern tied to input complexity. Understanding the relationship matters for deciding when LLM annotations are reliable.
domain specialization adds another axis of degradation beyond structural complexity
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Why do LLM persona prompts produce inconsistent outputs across runs?
Can language models reliably simulate different social perspectives through persona prompting, or does their run-to-run variance indicate they lack stable group-specific knowledge? This matters for whether LLMs can approximate human disagreement in annotation tasks.
the persona-based approach to capturing opinion distributions also fails for the same reason
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Original note title
llm overconfidence in domain-specific inference tasks persists in low-resource knowledge-rich domains