New research reveals LLMs lack self-awareness on clinical data

A new research paper explores the limitations of large language models (LLMs) when applied to structured clinical data, focusing on their inability to recognize their own knowledge gaps. The study found that LLM confidence scores are unreliable, often not correlating with accuracy. Furthermore, LLMs perform worse when traditional models like XGBoost are highly confident, but match performance when XGBoost is moderately uncertain. The research also demonstrated that few-shot examples and feature evidence are independent interventions that significantly improve accuracy and reduce attribution disagreement. AI

IMPACT Highlights the need for improved epistemic self-awareness in LLMs for reliable deployment in critical domains like healthcare.

RANK_REASON The cluster contains a research paper published on arXiv detailing novel findings about LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Attribution Disagreement Score
• Hugging Face
• Qwen 2.5 7B
• Shap
• XGBoost

• paper
• safety

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