Quantitative Introspection in Language Models: Tracking Internal States Across Conversation
Quantitative Introspection in Language Models: Tracking Internal States Across Conversation explores Develops a novel method for tracking internal emotive states in LLMs using their own numeric self-reports, offering a new avenue for AI safety and interpretability.. Commercial viability score: 5/10 in LLM Interpretability.
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