Researchers have investigated the confidence signals of large language models (LLMs) across different temporal stages of problem-solving, distinguishing between pre-solve "Feeling-of-Knowing" (FOK) and post-solve "Judgement-of-Learning" (JOL). Their findings indicate that while post-solve confidence generally offers better epistemic alignment, pre-solve confidence is cheaper and shows some calibration. The study also revealed that confidence calibration transfers well between English and Marathi through translation but degrades significantly for locally specific Marathi knowledge, suggesting linguistic transfer alone is insufficient for regional information accuracy. Furthermore, LLMs tend to maintain high confidence even when generating hallucinated responses, pointing to limitations in their self-assessment capabilities. AI
IMPACT Highlights limitations in LLM self-assessment and the need for better region-specific knowledge handling.
RANK_REASON Academic paper published on arXiv detailing LLM confidence assessment.
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