Summary of Improving Uncertainty Quantification in Large Language Models Via Semantic Embeddings, by Yashvir S. Grewal et al.
Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings
by Yashvir S. Grewal, Edwin V. Bonilla, Thang D. Bui
First submitted to arxiv on: 30 Oct 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 This paper proposes a novel approach for accurately quantifying uncertainty in large language models (LLMs), specifically designed to address the limitations of current state-of-the-art methods. The existing methods rely on strict bidirectional entailment criteria, sequence likelihoods, and often overestimate uncertainty due to minor wording differences, additional correct information, and non-important words. The proposed approach leverages semantic embeddings to achieve smoother and more robust estimation of semantic uncertainty in LLMs, capturing semantic similarities without depending on sequence likelihoods. This reduces biases introduced by irrelevant words in the answers. The authors also introduce an amortised version of their approach, modelling semantics as latent variables in a joint probabilistic model, allowing for uncertainty estimation in the embedding space with a single forward pass. This significantly reduces computational overhead compared to existing multi-pass methods. Experiments across multiple question-answering datasets and frontier LLMs demonstrate that the proposed embedding-based methods provide more accurate and nuanced uncertainty quantification than traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make big language models better by making them less uncertain. Right now, it’s hard to tell when these models are really sure about an answer or just guessing. The problem is that current methods for measuring uncertainty in these models are too sensitive and often get things wrong. This new approach uses special math to make the model understand how similar different answers are, without worrying about tiny details like word order. It’s like using a special filter to smooth out noise. This helps the model be more accurate and reliable, especially when it matters most. |
Keywords
» Artificial intelligence » Embedding » Embedding space » Probabilistic model » Question answering » Semantics
