# Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

**Authors:** Yuheng Huang, Jiayang Song, Zhijie Wang, Huaming Chen, Lei Ma  
**arXiv:** [2307.10236](https://arxiv.org/abs/2307.10236)  
**Date:** 2023-07-16  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2307.10236v1/

## Key points

- Paper titled "Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models"
- Explores advancements and challenges in Large Language Models (LLMs)
- Examines twelve uncertainty estimation methods across four NLP tasks using four LLMs
- Addresses concerns about trustworthiness of LLMs
- Demonstrates effectiveness of uncertainty estimation in identifying uncertain or non-factual predictions by LLMs
- Shows potential to uncover buggy programs in code generation tasks
- Enhances understanding of uncertainty measurement in LLMs
- Paves the way for further advancements to improve trustworthiness in real-world applications

## Summary

In their paper titled "Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models," authors Yuheng Huang, Jiayang Song, Zhijie Wang, Huaming Chen, and Lei Ma delve into the recent advancements in Large Language Models (LLMs) and the associated challenges they pose. The study explores twelve uncertainty estimation methods across four prominent natural language processing (NLP) tasks using four different LLMs to address concerns about trustworthiness. The findings demonstrate the effectiveness of uncertainty estimation in identifying uncertain or non-factual predictions made by LLMs. Experiments on code generation tasks using four LLMs on two datasets also indicate its potential to uncover buggy programs. This research enhances our understanding of uncertainty measurement in LLMs and paves the way for further advancements aimed at improving their trustworthiness in real-world applications.