Summary of Uncertainty Quantification For In-context Learning Of Large Language Models, by Chen Ling et al.
Uncertainty Quantification for In-Context Learning of Large Language Models
by Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 delves into the predictive uncertainty of Large Language Models (LLMs) during in-context learning, a capability that has revolutionized various fields by providing task-relevant demonstrations. Existing works have focused on quantifying uncertainty in LLM responses, but overlooked the complex nature of LLMs and in-context learning. The authors propose a novel formulation and estimation method to quantify both aleatoric (demonstration-related) and epistemic (model configuration-related) uncertainties. The proposed method allows for unsupervised understanding of in-context learning predictions in a plug-and-play fashion. The authors demonstrate the effectiveness of their decomposition through extensive experiments. This research contributes to a better understanding of LLMs’ predictive uncertainty, enabling more trustworthy applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can learn new things by looking at examples. Right now, these computers are very good at learning from just a few examples, which has helped them get really good at many tasks. However, sometimes they make mistakes or say things that aren’t true. The researchers in this paper want to understand why this happens and how we can make the computers more accurate. They propose a new way to measure how sure the computer is about what it’s saying. This helps us understand when the computer might be making a mistake. The authors test their idea and show that it works well. |
Keywords
* Artificial intelligence * Unsupervised
