IdealGPT: Iteratively Decomposing Vision and Language Reasoning via Large Language Models

2023-05-24 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, short

Summary

IdealGPT is a novel framework designed to enhance zero-shot vision-and-language (VL) reasoning by iteratively decomposing complex tasks using large language models (LLMs). It addresses limitations in prior approaches, which often depend on domain-specific sub-question models or prematurely force final answers. IdealGPT employs a three-module pipeline: an LLM generates sub-questions, a vision-and-language model (VLM) provides corresponding sub-answers, and a second LLM synthesizes these to derive the final answer. This divide-and-conquer procedure repeats until the system achieves sufficient confidence in its conclusion. Evaluated in zero-shot settings, IdealGPT significantly outperforms leading GPT-4-like models, achieving an absolute 10% improvement on VCR and a 15% improvement on SNLI-VE. The code for IdealGPT, submitted on 24 May 2023 and last revised 18 Jun 2026 (v3), is publicly available.

Key takeaway

For Machine Learning Engineers developing zero-shot vision-and-language reasoning systems, you should consider adopting an iterative decomposition framework like IdealGPT. This approach, which orchestrates LLMs and VLMs to break down complex problems into sub-questions and refine answers, can significantly boost performance. You could achieve absolute gains of 10% on VCR and 15% on SNLI-VE compared to current GPT-4-like models, enhancing the reliability of your multi-step inference applications. Explore the available code to integrate this method.

Key insights

IdealGPT iteratively decomposes vision-and-language reasoning tasks using LLMs and VLMs to improve zero-shot performance.

Principles

• Zero-shot VL reasoning benefits from multi-step decomposition.
• Iterative sub-questioning and answering enhances reasoning confidence.
• LLMs can orchestrate VL reasoning without domain-specific sub-models.

Method

An LLM generates sub-questions, a VLM provides sub-answers, and another LLM synthesizes for the final answer, repeating until confident.

In practice

• Achieve 10% higher VCR and 15% higher SNLI-VE scores than GPT-4-like models.
• Implement the IdealGPT framework for complex VL reasoning tasks.

Topics

• IdealGPT
• Vision-Language Reasoning
• Large Language Models
• Zero-shot Learning
• Iterative Decomposition
• Multi-step Inference

Code references

• Hxyou/IdealGPT

Best for: Research Scientist, AI Engineer, Computer Vision Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.