RACC defines six representation-aware coverage criteria that score jailbreak test suites by measuring activation of safety concepts extracted from LLM hidden states on a calibration set.
A comprehensive survey of llm alignment techniques: Rlhf, rlaif, ppo, dpo and more
2 Pith papers cite this work. Polarity classification is still indexing.
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A multi-dimensional audit framework for politically aligned LLMs finds consistent trade-offs: larger models are more effective and truthful but less fair with higher bias, while fine-tuned models reduce bias but increase hallucinations and reasoning decline, and all tested models show deficiencies.
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RACC: Representation-Aware Coverage Criteria for LLM Safety Testing
RACC defines six representation-aware coverage criteria that score jailbreak test suites by measuring activation of safety concepts extracted from LLM hidden states on a calibration set.
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A Multi-Dimensional Audit of Politically Aligned Large Language Models
A multi-dimensional audit framework for politically aligned LLMs finds consistent trade-offs: larger models are more effective and truthful but less fair with higher bias, while fine-tuned models reduce bias but increase hallucinations and reasoning decline, and all tested models show deficiencies.