Summary of Addressing Uncertainty in Llms to Enhance Reliability in Generative Ai, by Ramneet Kaur et al.
Addressing Uncertainty in LLMs to Enhance Reliability in Generative AI
by Ramneet Kaur, Colin Samplawski, Adam D. Cobb, Anirban Roy, Brian Matejek, Manoj Acharya, Daniel Elenius, Alexander M. Berenbeim, John A. Pavlik, Nathaniel D. Bastian, Susmit Jha
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 dynamic semantic clustering approach, inspired by the Chinese Restaurant Process, aims to address uncertainty in Large Language Models (LLMs) by quantifying their uncertainty on a given query. The entropy of generated semantic clusters is used to calculate uncertainty, and negative likelihoods are leveraged as nonconformity scores within the Conformal Prediction framework. This allows the model to predict a set of responses instead of a single output, accounting for uncertainty in its predictions. The approach is demonstrated on COQA and TriviaQA question answering benchmarks using Llama2 and Mistral LLMs, achieving state-of-the-art performance in uncertainty quantification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to predict answers from language models. Instead of giving just one answer, it suggests providing multiple possible answers along with their likelihood of being correct. This is done by calculating how uncertain the model is about its predictions and using that information to decide which answers to include. The method was tested on two question-answering datasets and performed better than other methods at doing this. |
Keywords
» Artificial intelligence » Clustering » Likelihood » Question answering
