AI RESEARCH
Enhancing Trust in Large Language Models via Uncertainty-Calibrated Fine-Tuning
arXiv CS.LG
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ArXi:2412.02904v2 Announce Type: replace-cross Large language models (LLMs) have revolutionized the field of natural language processing with their impressive reasoning and question-answering capabilities. However, these models are sometimes prone to generating credible-sounding but incorrect information, a phenomenon known as LLM hallucinations. Reliable uncertainty estimation in LLMs is essential for fostering trust in their generated responses and serves as a critical tool for the detection and prevention of erroneous or hallucinated outputs.