LLMEval-Logic is a solver-verified Chinese logical reasoning benchmark with 246 base and 190 hard items that shows frontier LLMs reach only 37.5% hard-item accuracy and 60.16% joint formalization score.
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DisaBench supplies a participatory taxonomy of twelve disability harm types, paired benign-adversarial prompts across seven life domains, and human-annotated data showing that standard safety tests miss context-dependent harms.
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LLMEval-Logic: A Solver-Verified Chinese Benchmark for Logical Reasoning of LLMs with Adversarial Hardening
LLMEval-Logic is a solver-verified Chinese logical reasoning benchmark with 246 base and 190 hard items that shows frontier LLMs reach only 37.5% hard-item accuracy and 60.16% joint formalization score.
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DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Models
DisaBench supplies a participatory taxonomy of twelve disability harm types, paired benign-adversarial prompts across seven life domains, and human-annotated data showing that standard safety tests miss context-dependent harms.