Summary of Efficient Non-parametric Uncertainty Quantification For Black-box Large Language Models and Decision Planning, by Yao-hung Hubert Tsai et al.
Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning
by Yao-Hung Hubert Tsai, Walter Talbott, Jian Zhang
First submitted to arxiv on: 1 Feb 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 method for decision planning with large language models (LLMs) that addresses the hallucination problem by estimating uncertainty. The approach is designed to work with black-box proprietary LLMs, which are often computationally demanding or require access to token logits. The method involves a non-parametric uncertainty quantification technique that estimates point-wise dependencies between input and decision on the fly, without requiring access to model internals. This allows for efficient estimation of decision trustworthiness. Additionally, the paper outlines a systematic design for a decision-making agent that generates actions based on user prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For non-technical audiences, this paper is about using artificial intelligence (AI) language models to make decisions. The problem with current AI models is that they can sometimes provide answers that are not accurate or relevant. This paper proposes a new way of working with these models to make more informed decisions. The method is designed to be efficient and work with popular AI models, even if the developers don’t want to share the underlying details. This approach has potential applications in areas like customer service chatbots or decision support systems. |
Keywords
* Artificial intelligence * Hallucination * Logits * Token
