# Enhancing Food-Domain Question Answering with a Multimodal Knowledge Graph: Hybrid QA Generation and Diversity Analysis

Source: [Enhancing Food-Domain Question Answering with a Multimodal Knowledge Graph: Hybrid QA Generation and Diversity Analysis](<https://www.sota2.com/research/paper/68fa79e81e209131cea7fe9e>)

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

Data updated: 2025-10-26T05:07:32.191Z

Authors: Srihari K B, Pushpak Bhattacharyya

Published: 2025-07-09T05:59:06.000Z

## Abstract

We propose a unified food-domain QA framework that combines a large-scale multimodal knowledge graph (MMKG) with generative AI. Our MMKG links 13,000 recipes, 3,000 ingredients, 140,000 relations, and 14,000 images. We generate 40,000 QA pairs using 40 templates and LLaVA/DeepSeek augmentation. Joint fine-tuning of Meta LLaMA 3.1-8B and Stable Diffusion 3.5-Large improves BERTScore by 16.2\\%, reduces FID by 37.8\\%, and boosts CLIP alignment by 31.1\\%. Diagnostic analyses-CLIP-based mismatch detection (35.2\\% to 7.3\\%) and LLaVA-driven hallucination checks-ensure factual and visual fidelity. A hybrid retrieval-generation strategy achieves 94.1\\% accurate image reuse and 85\\% adequacy in synthesis. Our results demonstrate that structured knowledge and multimodal generation together enhance reliability and diversity in food QA.

## 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/2507.06571>)

[PDF](<https://arxiv.org/pdf/2507.06571.pdf>)