Enhancing Trust in Large Language Models: Fine-Tuning for Calibrated Uncertainties in High-Stakes Applications

Large language models (LLMs) face a significant challenge in accurately representing uncertainty over the correctness of their output. This issue is critical for decision-making applications, particularly in fields like healthcare where erroneous confidence can lead to dangerous outcomes. The task is further complicated by linguistic variances in freeform generation, which cannot be exhaustively accounted for […]

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