GPQA is a new graduate-level benchmark where PhD experts score 65% (74% after corrections), skilled non-experts score 34% with web access, and GPT-4 scores 39%, intended to enable realistic tests of human supervision over superhuman AI.
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3 Pith papers cite this work. Polarity classification is still indexing.
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A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
Short-form factual consistency metrics produce inconsistent scores on semantically equivalent long-document summaries and lose reliability on information-dense claims.
citing papers explorer
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Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
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Stress Testing Factual Consistency Metrics for Long-Document Summarization
Short-form factual consistency metrics produce inconsistent scores on semantically equivalent long-document summaries and lose reliability on information-dense claims.