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Embedding Perturbation may Better Reflect Intermediate-Step Uncertainty in LLM Reasoning
Large Language Models (LLMs) have achieved significant breakthroughs across various domains, but they can still produce unreliable or misleading outputs. For responsible LLM applications, uncertainty quantification techniques are used to estimate a model's uncertainty about its outputs, indicating t
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