VB-Score shows three major LLMs have severe failures in medical entity recognition and factual consistency, with 13.8% lower performance on chronic conditions affecting older and minority groups, indicating condition-based algorithmic discrimination.
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CausalSynth combines structural causal models with LLMs and iterative verification to produce synthetic data that respects given causal structures while remaining linguistically natural.
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Beyond Semantic Similarity: A Component-Wise Evaluation Framework for Medical Question Answering Systems with Health Equity Implications
VB-Score shows three major LLMs have severe failures in medical entity recognition and factual consistency, with 13.8% lower performance on chronic conditions affecting older and minority groups, indicating condition-based algorithmic discrimination.
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CasualSynth: Generating Structurally Sound Synthetic Data
CausalSynth combines structural causal models with LLMs and iterative verification to produce synthetic data that respects given causal structures while remaining linguistically natural.