This episode examines the difficulties in accurately assessing the reliability of large language models (LLMs) when following instructions.
The episode highlights the limitations of current uncertainty estimation techniques and introduces a new framework called RLACE, which utilizes contrastive prompts to evaluate LLM instruction-following abilities.
The study found that even advanced LLMs like GPT-3.5 and GPT-4 sometimes struggle to follow complex or ambiguous instructions, suggesting the need for improved uncertainty estimation methods to ensure the safety and reliability of LLMs in real-world applications.

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