HealthContradict: Evaluating biomedical knowledge conflicts in language models
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By
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Boya Zhang
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Alban Bornet
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Rui Yang
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Nan Liu
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Douglas Teodoro
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January 21, 2026
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Clinical Scorecard: Assessing Conflicts in Biomedical Knowledge Within Language Models
At a Glance
| Category | Detail |
|---|
| Condition | Conflicting biomedical knowledge impacting language model outputs |
| Key Mechanisms | Contradictions between parametric knowledge and contextual knowledge in language models leading to confusion and misinformation |
| Target Population | Users seeking biomedical information from language models |
| Care Setting | Biomedical information retrieval and clinical decision support environments |
Key Highlights
- Language models can generate plausible but nonfactual biomedical content due to conflicting knowledge sources.
- Knowledge conflicts arise from contradictions between pre-trained parametric knowledge and external contextual information.
- Existing mitigation strategies focus on either parametric or contextual knowledge but integrating both improves model reliability.
Guideline-Based Recommendations
Diagnosis
- Recognize that language models may produce conflicting or outdated biomedical information due to knowledge conflicts.
Management
- Employ retrieval-augmented generation (RAG) pipelines combining verified evidence and relevance-focused retrieval to reduce hallucinations.
- Use context-aware decoding and discriminators to assess compatibility between parametric and contextual knowledge.
- Apply knowledge-aware fine-tuning with counterfactual and irrelevant contexts to enhance model robustness.
Monitoring & Follow-up
- Continuously evaluate language model outputs against updated biomedical evidence to detect misinformation.
- Utilize datasets like HealthContradict to benchmark model performance on contradictory biomedical questions.
Risks
- Risk of misleading health-related decisions due to convincing but incorrect language model outputs.
- Rapidly evolving biomedical knowledge can cause models to recite outdated or conflicting information.
Patient & Prescribing Data
Individuals seeking biomedical information via language models
No direct prescribing data; highlights importance of verifying language model outputs with current scientific evidence to avoid misinformation.
Clinical Best Practices
- Integrate both parametric and contextual knowledge sources when using language models for biomedical queries.
- Implement multi-faceted mitigation strategies including retrieval augmentation, context-aware decoding, and fine-tuning.
- Use specialized datasets to evaluate and improve model handling of biomedical knowledge conflicts.
- Maintain vigilance for misinformation and update models regularly to reflect current biomedical knowledge.
References
