This episode explores the challenges of uncertainty estimation in large language models (LLMs) for instruction-following tasks. While LLMs show promise as personal AI agents, they often struggle to accurately assess their uncertainty, leading to deviations from guidelines. The episode highlights the limitations of existing uncertainty methods, like semantic entropy, which focus on fact-based tasks rather than instruction adherence.Key findings from the evaluation of six uncertainty estimation methods across four LLMs reveal that current approaches struggle with subtle instruction-following errors. The episode introduces a new benchmark dataset with Controlled and Realistic versions to address the limitations of existing datasets, ensuring a more accurate evaluation of uncertainty.The discussion also covers the performance of various methods, with self-evaluation excelling in simpler tasks and logit-based approaches showing promise in more complex ones. Smaller models sometimes outperform larger ones in self-evaluation, and internal probing of model states proves effective. The episode concludes by emphasizing the need for further research to improve uncertainty estimation and ensure trustworthy AI agents.https://arxiv.org/pdf/2410.14582

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