SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning

• URL: http://arxiv.org/abs/2501.03675v2
• Date: Sat, 15 Feb 2025 00:15:41 GMT
• Title: SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning
• Authors: Andrew Li, Rahul Thapa, Rahul Chalamala, Qingyang Wu, Kezhen Chen, James Zou,
• Abstract summary: We introduce SMiR, a synthetic data-generation pipeline for multi-image reasoning. We produce 160K synthetic training samples, offering a cost-effective alternative to closed-source solutions. We also present SMiR-Bench, a multi-image reasoning benchmark comprising 200 diverse examples.
• Score: 26.986638043619397
• License:
• Abstract: Vision-Language Models (VLMs) excel at understanding single images, aided by high-quality instruction datasets. However, multi-image reasoning remains underexplored in the open-source community due to two key challenges: (1) scaling datasets with correlated images and complex reasoning instructions is resource-intensive, and (2) robust evaluation benchmarks for multi-image tasks are lacking. To address this, we introduce SMiR, a synthetic data-generation pipeline for multi-image reasoning, along with a high-quality dataset generated using this pipeline. SMiR efficiently extracts correlated images via multimodal embeddings, integrates visual and descriptive information, and leverages open-source LLMs to generate quality instructions. Using this approach, we produce 160K synthetic training samples, offering a cost-effective alternative to closed-source solutions. Additionally, we present SMiR-Bench, a multi-image reasoning benchmark comprising 200 diverse examples across seven complex reasoning tasks. SMiR-Bench is multi-turn and employs a VLM judge to evaluate free-form responses, providing a comprehensive assessment of model expressiveness and reasoning capability across modalities. We demonstrate the effectiveness of SMiR by fine-tuning open-source VLMs and evaluating them on SMiR-Bench.

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