A Survey of Calibration Process for Black-Box LLMs

TL;DR

This paper provides the first comprehensive survey of calibration techniques specifically designed for black-box large language models, addressing their unique challenges and outlining future research directions.

Contribution

It systematically reviews calibration methods for black-box LLMs, defining the calibration process, and discusses challenges, applications, and future research avenues.

Findings

Identifies key steps: Confidence Estimation and Calibration.

Highlights unique challenges in black-box settings.

Provides insights into applications and future directions.

Abstract

Large Language Models (LLMs) demonstrate remarkable performance in semantic understanding and generation, yet accurately assessing their output reliability remains a significant challenge. While numerous studies have explored calibration techniques, they primarily focus on White-Box LLMs with accessible parameters. Black-Box LLMs, despite their superior performance, pose heightened requirements for calibration techniques due to their API-only interaction constraints. Although recent researches have achieved breakthroughs in black-box LLMs calibration, a systematic survey of these methodologies is still lacking. To bridge this gap, we presents the first comprehensive survey on calibration techniques for black-box LLMs. We first define the Calibration Process of LLMs as comprising two interrelated key steps: Confidence Estimation and Calibration. Second, we conduct a systematic review of applicable methods within black-box settings, and provide insights on the unique challenges and connections in implementing these key steps. Furthermore, we explore typical applications of Calibration Process in black-box LLMs and outline promising future research directions, providing new perspectives for enhancing reliability and human-machine alignment. This is our GitHub link: https://github.com/LiangruXie/Calibration-Process-in-Black-Box-LLMs