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Can LLMs Follow Instructions Reliably? A Look at Uncertainty Estimation Challenges
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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By Michael IversenThis 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.