Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks

• URL: http://arxiv.org/abs/2507.18932v1
• Date: Fri, 25 Jul 2025 03:58:07 GMT
• Title: Benchmarking Multimodal Understanding and Complex Reasoning for ESG Tasks
• Authors: Lei Zhang, Xin Zhou, Chaoyue He, Di Wang, Yi Wu, Hong Xu, Wei Liu, Chunyan Miao,
• Abstract summary: Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency.<n>MMESGBench is a first-of-its-kind benchmark dataset to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents.<n>MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories.
• Score: 56.350173737493215
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Environmental, Social, and Governance (ESG) reports are essential for evaluating sustainability practices, ensuring regulatory compliance, and promoting financial transparency. However, these documents are often lengthy, structurally diverse, and multimodal, comprising dense text, structured tables, complex figures, and layout-dependent semantics. Existing AI systems often struggle to perform reliable document-level reasoning in such settings, and no dedicated benchmark currently exists in ESG domain. To fill the gap, we introduce \textbf{MMESGBench}, a first-of-its-kind benchmark dataset targeted to evaluate multimodal understanding and complex reasoning across structurally diverse and multi-source ESG documents. This dataset is constructed via a human-AI collaborative, multi-stage pipeline. First, a multimodal LLM generates candidate question-answer (QA) pairs by jointly interpreting rich textual, tabular, and visual information from layout-aware document pages. Second, an LLM verifies the semantic accuracy, completeness, and reasoning complexity of each QA pair. This automated process is followed by an expert-in-the-loop validation, where domain specialists validate and calibrate QA pairs to ensure quality, relevance, and diversity. MMESGBench comprises 933 validated QA pairs derived from 45 ESG documents, spanning across seven distinct document types and three major ESG source categories. Questions are categorized as single-page, cross-page, or unanswerable, with each accompanied by fine-grained multimodal evidence. Initial experiments validate that multimodal and retrieval-augmented models substantially outperform text-only baselines, particularly on visually grounded and cross-page tasks. MMESGBench is publicly available as an open-source dataset at https://github.com/Zhanglei1103/MMESGBench.

Related papers

• Docopilot: Improving Multimodal Models for Document-Level Understanding [87.60020625241178]
  We present a high-quality document-level dataset, Doc-750K, designed to support in-depth understanding of multimodal documents.<n>This dataset includes diverse document structures, extensive cross-page dependencies, and real question-answer pairs derived from the original documents.<n>Building on the dataset, we develop a native multimodal model, Docopilot, which can accurately handle document-level dependencies without relying on RAG.
  arXiv  Detail & Related papers  (2025-07-19T16:03:34Z)
• Beyond Retrieval: Joint Supervision and Multimodal Document Ranking for Textbook Question Answering [3.6799953119508735]
  We propose a novel approach to multimodal textbook question answering by introducing a mechanism for enhancing semantic representations.<n>Our model, Joint Embedding Training With Ranking Supervision for Textbook Question Answering (JETRTQA), is a multimodal learning framework built on a retriever-generator architecture.<n>We evaluate our method on the CK12-QA dataset and demonstrate that it significantly improves the discrimination between informative and irrelevant documents.
  arXiv  Detail & Related papers  (2025-05-17T13:23:54Z)
• MEBench: Benchmarking Large Language Models for Cross-Document Multi-Entity Question Answering [0.0]
  Multi-entity question answering (MEQA) represents significant challenges for large language models (LLM)<n>We introduce MEBench, a novel multi-document, multi-entity benchmark designed to evaluate LLMs' capacity to retrieve, consolidate, and reason over fragmented information.<n>Our benchmark emphasizes the importance of completeness and factual precision of information extraction in MEQA tasks.
  arXiv  Detail & Related papers  (2025-02-26T09:56:51Z)
• OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models [58.45517851437422]
  Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understanding.<n>Existing solutions often rely on task-specific architectures and objectives for individual tasks.<n>In this paper, we introduce Omni V2, a universal model that unifies VsTP typical tasks, including text spotting, key information extraction, table recognition, and layout analysis.
  arXiv  Detail & Related papers  (2025-02-22T09:32:01Z)
• MMDocIR: Benchmarking Multi-Modal Retrieval for Long Documents [26.39534684408116]
  This work introduces a new benchmark, named MMDocIR, that encompasses two distinct tasks: page-level and layout-level retrieval.<n>The MMDocIR benchmark comprises a rich dataset featuring 1,685 questions annotated by experts and 173,843 questions with bootstrapped labels.
  arXiv  Detail & Related papers  (2025-01-15T14:30:13Z)
• VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation [100.06122876025063]
  This paper introduces VisDoMBench, the first comprehensive benchmark designed to evaluate QA systems in multi-document settings.<n>We propose VisDoMRAG, a novel multimodal Retrieval Augmented Generation (RAG) approach that simultaneously utilizes visual and textual RAG.
  arXiv  Detail & Related papers  (2024-12-14T06:24:55Z)
• M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding [63.33447665725129]
  We introduce M3DocRAG, a novel multi-modal RAG framework that flexibly accommodates various document contexts. M3DocRAG can efficiently handle single or many documents while preserving visual information. We also present M3DocVQA, a new benchmark for evaluating open-domain DocVQA over 3,000+ PDF documents with 40,000+ pages.
  arXiv  Detail & Related papers  (2024-11-07T18:29:38Z)
• The Power of Summary-Source Alignments [62.76959473193149]
  Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection. alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data. This paper proposes extending the summary-source alignment framework by applying it at the more fine-grained proposition span level.
  arXiv  Detail & Related papers  (2024-06-02T19:35:19Z)
• PDF-MVQA: A Dataset for Multimodal Information Retrieval in PDF-based Visual Question Answering [13.625303311724757]
  Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD) We propose PDF-MVQA, which is tailored for research journal articles, encompassing multiple pages and multimodal information retrieval.
  arXiv  Detail & Related papers  (2024-04-19T09:00:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.