Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation
Yiming Wang Pei Zhang Baosong Yang Derek F. Wong Rui Wang
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. Experiments in four diverse domains and seven LLMs fully demonstrate the effectiveness of our method. Meanwhile, its label-free design intent without any training and millisecond-level computational cost ensure real-time feedback in large-scale scenarios.
More importantly, we provide interesting insights into LLM response correctness from the perspective of hidden state changes inside LLMs.
Large Language Models, Label-free Self-Evaluation, AI Reliability
We propose the Chain-of-Embedding method for LLM self-evaluation, which enables output-free response correctness estimation during inference time.