# ChatGPT-Powered Hierarchical Comparisons for Image Classification

Source: [ChatGPT-Powered Hierarchical Comparisons for Image Classification](<https://www.sota2.com/research/paper/68fa71121e209131cea4f10c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:57:41.618Z

Authors: Zhiyuan Ren, Yiyang Su, Xiaoming Liu

Published: 2023-11-01T00:26:40.000Z

## Abstract

The zero-shot open-vocabulary challenge in image classification is tackled by pretrained vision-language models like CLIP, which benefit from incorporating class-specific knowledge from large language models (LLMs) like ChatGPT. However, biases in CLIP lead to similar descriptions for distinct but related classes, prompting our novel image classification framework via hierarchical comparisons: using LLMs to recursively group classes into hierarchies and classifying images by comparing image-text embeddings at each hierarchy level, resulting in an intuitive, effective, and explainable approach.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2311.00206>)

[PDF](<https://arxiv.org/pdf/2311.00206.pdf>)