Summary of A Survey Of Calibration Process For Black-box Llms, by Liangru Xie et al.
A Survey of Calibration Process for Black-Box LLMs
by Liangru Xie, Hui Liu, Jingying Zeng, Xianfeng Tang, Yan Han, Chen Luo, Jing Huang, Zhen Li, Suhang Wang, Qi He
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The proposed survey provides a comprehensive overview of calibration techniques for black-box Large Language Models (LLMs). The Calibration Process is composed of Confidence Estimation and Calibration, which are essential for assessing the reliability of LLMs’ output. The authors conducted a systematic review of existing methods within black-box settings, highlighting unique challenges and connections between key steps. The survey also explores typical applications of Calibration Process in black-box LLMs and outlines promising future research directions to enhance reliability and human-machine alignment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Calibration techniques for black-box Large Language Models (LLMs) are important for understanding how well these models work. This survey looks at different methods that can be used to calibrate black-box LLMs, which are hard to understand because of limited access to their internal workings. The authors define a process called Calibration Process, which has two main steps: Confidence Estimation and Calibration. They then review existing methods for using these steps in black-box settings, discussing challenges and connections between them. This helps to identify applications and future research directions. |
Keywords
» Artificial intelligence » Alignment
