Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation

Understand

LLM self-evaluation relies on the LLM's own ability to estimate response correctness, which can greatly improve its deployment reliability.

• In this research track, we propose the Chain-of-Embedding (CoE) in the latent space to enable LLMs to perform output-free self-evaluation.
• CoE consists of all progressive hidden states produced during the inference time, which can be treated as the latent thinking path of LLMs.
• We find that when LLMs respond correctly and incorrectly, their CoE features differ, these discrepancies assist us in estimating LLM response correctness.