# Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering

Source: [Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answering](<https://www.sota2.com/research/paper/68fa74a21e209131cea62d06>)

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

Data updated: 2025-10-26T03:14:35.342Z

Authors: Su Hyeon Lim, Minkuk Kim, Hyeon Bae Kim, Seong Tae Kim

Published: 2024-08-30T04:39:43.000Z

## Abstract

Visual Question Answering with Natural Language Explanation (VQA-NLE) task is challenging due to its high demand for reasoning-based inference. Recent VQA-NLE studies focus on enhancing model networks to amplify the model's reasoning capability but this approach is resource-consuming and unstable. In this work, we introduce a new VQA-NLE model, ReRe (Retrieval-augmented natural language Reasoning), using leverage retrieval information from the memory to aid in generating accurate answers and persuasive explanations without relying on complex networks and extra datasets. ReRe is an encoder-decoder architecture model using a pre-trained clip vision encoder and a pre-trained GPT-2 language model as a decoder. Cross-attention layers are added in the GPT-2 for processing retrieval features. ReRe outperforms previous methods in VQA accuracy and explanation score and shows improvement in NLE with more persuasive, reliability.

## 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/2408.17006>)

[PDF](<https://arxiv.org/pdf/2408.17006.pdf>)