New method decodes causal reasoning in LLM hidden states

Researchers have developed a method to analyze how language models interpret causal questions based on diagnostic evidence. By using paired prompts that alter the causal target while keeping the evidence verbatim, they can decode whether the model favors, challenges, or fails to address the claim. This analysis, applied to models like Qwen2.5-7B-Instruct and Llama 3.1 8B-Instruct, reveals that the models' hidden states contain linearly decodable information about causal reasoning, outperforming simpler baselines. AI

IMPACT Provides a method to probe LLM reasoning capabilities, potentially improving interpretability and trustworthiness.

RANK_REASON Research paper detailing a new method for analyzing LLM states. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• Llama 3.1 8B-Instruct
• Qwen2.5-7B-Instruct
• Qwen3_8B
• ScienceCast

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