II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering

TL;DR

This paper introduces II-MMR, a method to identify and enhance multi-modal multi-hop reasoning in VQA, revealing that most questions are simple and improving reasoning on complex questions using novel prompts.

Contribution

II-MMR proposes new prompts to analyze and improve multi-hop reasoning in VQA, addressing limitations of traditional Chain-of-Thought prompting.

Findings

Most VQA questions are single-hop reasoning.

II-MMR effectively improves multi-hop reasoning performance.

Traditional CoT struggles with complex multi-hop questions.

Abstract

Visual Question Answering (VQA) often involves diverse reasoning scenarios across Vision and Language (V&L). Most prior VQA studies, however, have merely focused on assessing the model's overall accuracy without evaluating it on different reasoning cases. Furthermore, some recent works observe that conventional Chain-of-Thought (CoT) prompting fails to generate effective reasoning for VQA, especially for complex scenarios requiring multi-hop reasoning. In this paper, we propose II-MMR, a novel idea to identify and improve multi-modal multi-hop reasoning in VQA. In specific, II-MMR takes a VQA question with an image and finds a reasoning path to reach its answer using two novel language promptings: (i) answer prediction-guided CoT prompt, or (ii) knowledge triplet-guided prompt. II-MMR then analyzes this path to identify different reasoning cases in current VQA benchmarks by estimating how many hops and what types (i.e., visual or beyond-visual) of reasoning are required to answer the question. On popular benchmarks including GQA and A-OKVQA, II-MMR observes that most of their VQA questions are easy to answer, simply demanding "single-hop" reasoning, whereas only a few questions require "multi-hop" reasoning. Moreover, while the recent V&L model struggles with such complex multi-hop reasoning questions even using the traditional CoT method, II-MMR shows its effectiveness across all reasoning cases in both zero-shot and fine-tuning settings.