Summary of Kernel Language Entropy: Fine-grained Uncertainty Quantification For Llms From Semantic Similarities, by Alexander Nikitin et al.
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
by Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. The paper proposes a novel method, Kernel Language Entropy (KLE), to estimate uncertainty in white- and black-box LLMs. KLE defines positive semidefinite unit trace kernels to encode semantic similarities of LLM outputs and quantifies uncertainty using the von Neumann entropy. It considers pairwise semantic dependencies between answers, providing more fine-grained uncertainty estimates than previous methods. The paper theoretically proves that KLE generalizes the state-of-the-art method called semantic entropy and empirically demonstrates its improved performance across multiple natural language generation datasets and LLM architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure large language models are reliable and trustworthy. It’s important because we don’t want these models to give us false information. The problem is that these models can sometimes make mistakes, called hallucinations. To fix this, the authors created a new way to measure how uncertain the model is about what it’s saying. This method is called Kernel Language Entropy (KLE). It helps by considering the relationships between different answers and providing more accurate uncertainty estimates. |
