A new research paper explores the monitorability of large language models (LLMs) when using Chain-of-Thought (CoT) reasoning, particularly focusing on latent CoT approaches that reduce inference costs by replacing explicit reasoning traces with continuous states. The study investigates how effectively these latent CoT methods can be monitored for specific behaviors, such as reliance on biasing input cues, by comparing explicit CoT with weakly and strongly supervised latent CoT on math reasoning and question answering tasks. The findings suggest that monitorability is more influenced by task characteristics and the level of access to model internals than by the specific reasoning mode employed. AI
IMPACT Investigates how latent CoT affects LLM interpretability, potentially impacting the development of safer and more transparent AI systems.
RANK_REASON Research paper published on arXiv discussing LLM monitorability. [lever_c_demoted from research: ic=1 ai=1.0]
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