Summary of Latent Space Chain-of-embedding Enables Output-free Llm Self-evaluation, by Yiming Wang et al.
Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation
by Yiming Wang, Pei Zhang, Baosong Yang, Derek F. Wong, Rui Wang
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel method for Large Language Models (LLMs) to perform output-free self-evaluation, enabling improved deployment reliability. The Chain-of-Embedding (CoE) in the latent space captures the LLM’s thought process during inference time, allowing for accurate estimation of response correctness. By analyzing CoE features when responses are correct or incorrect, the method demonstrates effectiveness across four domains and seven LLMs. This label-free design offers real-time feedback at a millisecond-level computational cost, making it suitable for large-scale scenarios. The study also provides insights into LLM response correctness from the perspective of hidden state changes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if a computer program could evaluate its own answers to make sure they’re correct. That’s what this research is about: helping computers learn to be more reliable. They came up with a new way for computers to think about their thoughts, called Chain-of-Embedding (CoE). By looking at the CoE, computers can figure out if their responses are right or wrong. The researchers tested this method on many different types of tasks and found it works really well. This is important because it could help us use these computer programs in more places, like real-time applications. |
Keywords
» Artificial intelligence » Embedding » Inference » Latent space
