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

Haoxuan You, Rui Sun, Zhecan Wang, Long Chen, Gengyu Wang, Hammad A. Ayyubi, Kai-Wei Chang, Shih-Fu Chang

arXiv:2305.14985v3cs.CVcs.CL

TL;DR

Zero-shot multi-step vision-language reasoning remains challenging, and earlier decomposition methods rely on task-specific questioners and presume one round will suffice. IdealGPT iteratively combines LLM question generation and reasoning with VLM sub-answers, outperforming GPT-4-like models on VCR and SNLI-VE.

  • Problem

    End-to-end VLMs struggle with zero-shot multi-step reasoning, and prior decomposition methods rely on task-specific questioners and assume one round provides sufficient evidence.

    Source: §1 · pp. 1–2

  • Method

    IdealGPT uses an LLM to generate sub-questions, a VLM to answer them, and another LLM to reason iteratively toward a confident answer.

    Source: §1 · p. 2, §3.1 · p. 4, §3.2 · p. 5

  • Results

    10% on VCR and 15% on SNLI-VE are the absolute margins by which IdealGPT outperforms the best existing GPT-4-like models.

    Source: §1 · p. 3, §5 · p. 9

  • Takeaways & Limitations

    The modular design permits updating its LLMs and VLM to more powerful models and supports zero-shot application across VL tasks.

    Source: §1 · p. 2

    Performance is largely bottlenecked by pretrained VLMs’ ability to answer key sub-questions correctly.

    Source: Limitations · p. 9

Abstract

from arXiv · show

The field of vision-and-language (VL) understanding has made unprecedented progress with end-to-end large pre-trained VL models (VLMs). However, they still fall short in zero-shot reasoning tasks that require multi-step inferencing. To achieve this goal, previous works resort to a divide-and-conquer pipeline. In this paper, we argue that previous efforts have several inherent shortcomings: 1) They rely on domain-specific sub-question decomposing models. 2) They force models to predict the final answer even if the sub-questions or sub-answers provide insufficient information. We address these limitations via IdealGPT, a framework that iteratively decomposes VL reasoning using large language models (LLMs). Specifically, IdealGPT utilizes an LLM to generate sub-questions, a VLM to provide corresponding sub-answers, and another LLM to reason to achieve the final answer. These three modules perform the divide-and-conquer procedure iteratively until the model is confident about the final answer to the main question. We evaluate IdealGPT on multiple challenging VL reasoning tasks under a zero-shot setting. In particular, our IdealGPT outperforms the best existing GPT-4-like models by an absolute 10% on VCR and 15% on SNLI-VE. Code is available at https://github.com/Hxyou/IdealGPT

1 Introduction

IdealGPT addresses zero-shot multi-step vision-language reasoning by iteratively decomposing questions with LLMs and a VLM; it outperforms GPT-4-like models on VCR and SNLI-VE.

1 Introduction

  • End-to-end VLMs predict in one step and struggle with zero-shot multi-step reasoning, while prior decomposition methods use task-specific questioners and assume one round suffices.Source: §1 · pp. 1–2
  • The authors frame decomposition as human-like divide-and-conquer: answer simpler questions about visual details, then combine those answers to address the main question.Source: §1 · pp. 1–2
  • IdealGPT assigns sub-question generation to an LLM Questioner, answers to a VLM, and synthesis to an LLM Reasoner; insufficient evidence prompts further targeted rounds.Source: §1 · p. 2
  • Reported advantages include stepwise interpretability, modularity, robustness to noisy predictions, and zero-shot generalizability across VL tasks.The Questioner/Reasoner and Answerer can be updated to more powerful LLMs and VLMs.Source: §1 · p. 2
  • IdealGPT outperforms the best existing GPT-4-like models by an absolute 10% on VCR and 15% on SNLI-VE.Source: §1 · p. 3

2 Related Works

Related work spans compositional question decomposition, end-to-end vision-language models, and LLM-assisted visual reasoning; IdealGPT is positioned as an iterative, multi-round alternative.

2.1 Compositional QA in Vision/Language

  • Compositional methods include self-consistency and reasoning-chain aggregation (Wang et al., 2022c; Yoran et al., 2023), VQA introspection (Selvaraju et al., 2020), and learned sub-questioning (Uehara et al., 2022; Wang et al., 2022b); existing systems remain task-specific and one-round.Source: §2.1 · p. 3

2.2 End-to-End Vision-Language Models

2.3 GPT-Aided Visual Reasoning

  • LLM-controller systems coordinate models or generate programs for visual tasks, while IdealGPT differs from one-round approaches by solving VL tasks iteratively across multiple rounds.Source: §2.3 · p. 3

3 Method

IdealGPT uses an iterative Questioner–Answerer–Reasoner loop for vision-and-language reasoning. When evidence is insufficient, it gathers supplementary sub-questions and continues until the Reasoner finds a confident answer or reaches a preset iteration limit.

3 Method

  • IdealGPT iterates among a Questioner, Answerer, and Reasoner until a confident answer is found or a predefined iteration limit is reached.Source: §3 · p. 4, §3.3 · p. 5

3.1 Questioner

  • The Questioner uses a VLM-generated caption to help GPT generate sub-questions, then uses prior questions, answers, and Reasoner analysis to target follow-up questions.Source: §3.1 · pp. 4–5

3.2 Answerer

  • A pretrained VLM answers each generated sub-question separately, without dataset finetuning, to provide evidence for the main question.Source: §3.2 · p. 5

3.3 Reasoner

  • The Reasoner analyzes the main question, caption, and accumulated sub-questions and answers; when uncertain, it triggers another round of supplementary questions.Source: §3.3 · p. 5

3.4 Comparison with Other Methods

  • Unlike VisProg and ViperGPT, which execute programs in one pass, IdealGPT iteratively refines sub-questions and can revise its final answer; unlike ChatCaptioner, it targets VL reasoning.Source: §3.4 · pp. 5–6

4 Experiments

Experiments assess IdealGPT on zero-shot VCR and SNLI-VE, then ablate its iterative design and VLM components. The reported comparisons favor IdealGPT over GPT-4-like baselines and show gains from iteration.

4.1 Experimental Setup

  • Evaluation uses 5,000 validation/development examples per dataset, accuracy, ChatGPT as Questioner/Reasoner, and three pretrained-only VLM Answerers.The VLMs are BLIP-2, MiniGPT4, and LLaVA; ChatGPT is accessed through the gpt-3.5turbo API.Source: §4.1 · p. 6

4.2 Visual Commonsense Reasoning

  • IdealGPT beats MiniGPT4 by over 10% on zero-shot VCR and surpasses MiniGPT4 and LLaVA by 20% and 15%, respectively, on SNLI-VE.The reported VCR result uses BLIP2-FlanT5-XL as Answerer.Source: Table 1 · p. 7, §4.2 · p. 7, Table 2 · p. 7
  • In VCR, a second pass asks about expressions and body language after weak first-pass evidence; sad expressions and distant body language support a confident answer.The added sub-questions collect more evidence for the Reasoner.Source: §4.2 · p. 7

4.3 Visual Entailment

  • SNLI-VE is three-way visual-entailment classification; IdealGPT's cross-format performance contrasts with BLIP-2's failure on the task.The paper notes that SNLI-VE and VCR have distinct formats.Source: §4.3 · pp. 7–8

4.4 Ablation Studies

  • Iterative decomposition boosts accuracy by as high as 6%; a four-pass cap balances inference time and effectiveness, averaging 1.8 passes.Max.#Iterations=1 denotes deterministic one-round answering without iterative decomposition.Source: Table 3 · p. 8, §4.4 · p. 8
  • Among Answerers, BLIP-2 handles detailed visual facts better; for captions, it performs best, while longer but less accurate MiniGPT4/LLaVA captions can mislead reasoning.The authors describe MiniGPT4/LLaVA captions as more informative but more prone to mistakes or hallucination.Source: Table 4 · p. 8, Table 5 · p. 8, §4.4 · p. 8

5 Conclusion

The conclusion identifies gaps in zero-shot reasoning and in requiring answers when evidence is insufficient, then presents IdealGPT as a multiple-pass framework. The authors report improved interpretability, robustness, and zero-shot performance.

  • IdealGPT targets the lack of zero-shot reasoning and the assumption that models should answer without sufficient information.Source: §5 · pp. 8–9
  • IdealGPT uses multiple passes among a Questioner, Answerer, and Reasoner to address these problems.Source: §5 · p. 9
  • 10% on VCR and 15% on SNLI-VE are the reported absolute margins over the best existing GPT-4-like models.Source: §5 · p. 9
  • The authors say the framework improves interpretability and robustness to correcting predictions such as hallucinations.Source: §5 · p. 9

Limitations

The authors identify dependence on pretrained VLM performance, caption informativeness, and manually designed prompts as limitations.

  • IdealGPT’s final results are largely bottlenecked by pretrained VLM performance because correct answers to key sub-questions matter.Source: Limitations · p. 9
  • The system uses general image captions, while the authors suggest dense captions might provide more information about unseen images.Source: Limitations · p. 9
  • Prompts are manually designed, and finding the optimal prompt for a specific situation is difficult.Source: Limitations · p. 9

Ethics Statement

The ethics statement flags potential racial and gender bias in ChatGPT and warns that LLM generations are difficult to control.

  • ChatGPT’s pretraining corpus is likely to contain potential racial and gender bias.Source: Ethics Statement · p. 9
  • The authors recommend checking for potential bias before using the system in a specific environment and warn about hallucinations.Source: Ethics Statement · p. 9

A Data Statistics

The appendix reports statistics for SNLI-VE and VCR; the experiments randomly sample 5,000 examples from each dataset’s validation or development split for zero-shot evaluation.

  • The statistics table covers SNLI-VE and VCR.Source: Table 6 · p. 11
  • The table caption expands C, N, E, and VL as Contradiction, Neural, Entailment, and Vision-Language.Source: Table 6 · p. 11
  • The experiments randomly select 5,000 samples from the VCR validation split and SNLI-VE development split.Source: A Data Statistics · p. 11

B Details of Visual Entailment Results

On SNLI-VE, IdealGPT outperforms MiniGPT4 and LLaVA by a large margin. MiniGPT4 is near chance, while IdealGPT surpasses supervised EVE-Image in the contradiction category.

C Details of Prompts Used

The appendix presents IdealGPT prompts for VCR and SNLI-VE and explains how instance-specific text fills prompt placeholders.

D VCR Pre-Processing

VCR preprocessing assigns mentioned people to left, middle, or right positions using three image-region bins. This covers most cases because most VCR questions mention fewer than three people.

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