Summary of Semantic Density: Uncertainty Quantification For Large Language Models Through Confidence Measurement in Semantic Space, by Xin Qiu et al.
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
by Xin Qiu, Risto Miikkulainen
First submitted to arxiv on: 22 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed framework in this paper addresses the issue of trustworthiness in Large Language Models (LLMs) by developing a new method for uncertainty/confidence quantification. The existing LLMs do not provide users with an inherent metric to evaluate their responses, making it difficult to assess their reliability. This limitation is addressed through the “Semantic Density” framework, which extracts uncertainty and confidence information from each response based on probability distributions in semantic space. Unlike previous approaches, this method has no restrictions on task types and can be used for new models and tasks without additional training or data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper develops a way to measure how sure a Large Language Model is about its answers. Right now, these models don’t give us any clues about their confidence level, making it hard to know if we should trust what they say. The new method proposed in this paper helps solve this problem by analyzing the model’s responses and providing an uncertainty metric for each answer. This way, we can better understand when the model is certain or unsure of its response. |
Keywords
» Artificial intelligence » Large language model » Probability
