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cs.CL2025
Teaching People LLM's Errors and Getting it Right
Nathan Stringham, Fateme Hashemi Chaleshtori, Xinyuan Yan +3
People use large language models (LLMs) when they should not. This is partly because they see LLMs compose poems and answer intricate questions, so they understandably, but incorre…
cs.CL2024
Chain-of-Thought Unfaithfulness as Disguised Accuracy
Oliver Bentham, Nathan Stringham, Ana MarasoviÄ
Understanding the extent to which Chain-of-Thought (CoT) generations align with a large language model's (LLM) internal computations is critical for deciding whether to trust an LL…