Summary of To Believe or Not to Believe Your Llm, by Yasin Abbasi Yadkori et al.
To Believe or Not to Believe Your LLM
by Yasin Abbasi Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers focus on uncertainty quantification in large language models (LLMs) to identify when responses are unreliable. They consider both epistemic and aleatoric uncertainties, where epistemic uncertainty stems from a lack of knowledge about the ground truth, and aleatoric uncertainty arises from irreducible randomness. The authors derive an information-theoretic metric to detect large epistemic uncertainty, which can be computed based on model outputs using special iterative prompting techniques. This quantification enables detection of hallucinations in single- and multi-answer responses. Experiments demonstrate the advantages of this formulation over standard uncertainty quantification strategies. Additionally, the study sheds light on how probabilities assigned by an LLM can be amplified through iterative prompting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can answer questions and generate text. But sometimes they make mistakes or give wrong answers. This paper tries to figure out when those mistakes happen and why. The researchers came up with a new way to measure how sure the model is about its answers, which helps them detect when the model is just making things up. They tested this method and found that it works better than other ways of measuring uncertainty. They also learned something interesting about how the model’s confidence in its answers can change when asked follow-up questions. |
Keywords
» Artificial intelligence » Prompting
