Analysis of the LMArena dataset reveals heavy topic skew and varying model rankings, leading to an interactive visualization tool for users to define custom evaluation priorities on LLM leaderboards.
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2026 2verdicts
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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.
citing papers explorer
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Who Defines "Best"? Towards Interactive, User-Defined Evaluation of LLM Leaderboards
Analysis of the LMArena dataset reveals heavy topic skew and varying model rankings, leading to an interactive visualization tool for users to define custom evaluation priorities on LLM leaderboards.
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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.