VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation
  for Multilingual Long Document Understanding
Most organizational data in this world are stored as documents, and visual
retrieval plays a crucial role in unlocking the collective intelligence from
all these documents. However, existing benchmarks focus on English-only
document retrieval or only consider multilingual question-answering on a
single-page image. To bridge this gap, we introduce VisR-Bench, a multilingual
benchmark designed for question-driven multimodal retrieval in long documents.
Our benchmark comprises over 35K high-quality QA pairs across 1.2K documents,
enabling fine-grained evaluation of multimodal retrieval. VisR-Bench spans
sixteen languages with three question types (figures, text, and tables),
offering diverse linguistic and question coverage. Unlike prior datasets, we
include queries without explicit answers, preventing models from relying on
superficial keyword matching. We evaluate various retrieval models, including
text-based methods, multimodal encoders, and MLLMs, providing insights into
their strengths and limitations. Our results show that while MLLMs
significantly outperform text-based and multimodal encoder models, they still
struggle with structured tables and low-resource languages, highlighting key
challenges in multilingual visual retrieval.
689ad246fab6fdd2e52ac52f
Jian Chen
claimed_verified
2025-08-13T07:19:17.687Z
64593836c16ecb4815e082ce
https://cdn-avatars.huggingface.co/v1/production/uploads/64593836c16ecb4815e082ce/T9WM0Ly6ADi3a9JUSIsSV.png
Jian Chen
user
puar-playground
689ad246fab6fdd2e52ac530
Ming Li
689ad246fab6fdd2e52ac531
Jihyung Kil
689ad246fab6fdd2e52ac532
Chenguang Wang
689ad246fab6fdd2e52ac533
Tong Yu
689ad246fab6fdd2e52ac534
Ryan Rossi
689ad246fab6fdd2e52ac535
Tianyi Zhou
claimed_verified
2025-08-25T08:28:08.784Z
647f5af5b0e96764589f3b2a
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/VJ4cDyjp5M3V5WmI5gPIU.jpeg
Tianyi Zhou
user
zhoutianyi
689ad246fab6fdd2e52ac536
Changyou Chen
689ad246fab6fdd2e52ac537
Ruiyi Zhang
VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation
  for Multilingual Long Document Understanding
Most organizational data in this world are stored as documents, and visual
retrieval plays a crucial role in unlocking the collective intelligence from
all these documents. However, existing benchmarks focus on English-only
document retrieval or only consider multilingual question-answering on a
single-page image. To bridge this gap, we introduce VisR-Bench, a multilingual
benchmark designed for question-driven multimodal retrieval in long documents.
Our benchmark comprises over 35K high-quality QA pairs across 1.2K documents,
enabling fine-grained evaluation of multimodal retrieval. VisR-Bench spans
sixteen languages with three question types (figures, text, and tables),
offering diverse linguistic and question coverage. Unlike prior datasets, we
include queries without explicit answers, preventing models from relying on
superficial keyword matching. We evaluate various retrieval models, including
text-based methods, multimodal encoders, and MLLMs, providing insights into
their strengths and limitations. Our results show that while MLLMs
significantly outperform text-based and multimodal encoder models, they still
struggle with structured tables and low-resource languages, highlighting key
challenges in multilingual visual retrieval.
VisR-Bench is a multilingual benchmark for question-driven multimodal retrieval in long documents, evaluating various models across different languages and question types.
multimodal benchmark question-driven retrieval long documents QA pairs multimodal encoders MLLMs structured tables low-resource languages