M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models

• URL: http://arxiv.org/abs/2411.04075v1
• Date: Wed, 06 Nov 2024 17:52:01 GMT
• Title: M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models
• Authors: Chuhan Li, Ziyao Shangguan, Yilun Zhao, Deyuan Li, Yixin Liu, Arman Cohan,
• Abstract summary: We introduce M3SciQA, a multi-modal, multi-document scientific question answering benchmark for evaluating foundation models. M3SciQA consists of 1,452 expert-annotated questions spanning 70 natural language processing paper clusters. Our results indicate that current foundation models still significantly underperform compared to human experts in multi-modal information retrieval and in reasoning across multiple scientific documents.
• Score: 27.910693214922052
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing benchmarks for evaluating foundation models mainly focus on single-document, text-only tasks. However, they often fail to fully capture the complexity of research workflows, which typically involve interpreting non-textual data and gathering information across multiple documents. To address this gap, we introduce M3SciQA, a multi-modal, multi-document scientific question answering benchmark designed for a more comprehensive evaluation of foundation models. M3SciQA consists of 1,452 expert-annotated questions spanning 70 natural language processing paper clusters, where each cluster represents a primary paper along with all its cited documents, mirroring the workflow of comprehending a single paper by requiring multi-modal and multi-document data. With M3SciQA, we conduct a comprehensive evaluation of 18 foundation models. Our results indicate that current foundation models still significantly underperform compared to human experts in multi-modal information retrieval and in reasoning across multiple scientific documents. Additionally, we explore the implications of these findings for the future advancement of applying foundation models in multi-modal scientific literature analysis.

Related papers

• 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) We introduce MEBench, a novel multi-document, multi-entity benchmark designed to evaluate LLMs' capacity to retrieve, consolidate, and reason over fragmented information. 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)
• Personalized Multimodal Large Language Models: A Survey [127.9521218125761]
  Multimodal Large Language Models (MLLMs) have become increasingly important due to their state-of-the-art performance and ability to integrate multiple data modalities. This paper presents a comprehensive survey on personalized multimodal large language models, focusing on their architecture, training methods, and applications.
  arXiv  Detail & Related papers  (2024-12-03T03:59:03Z)
• Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong Baselines [63.427721165404634]
  This paper investigates an intriguing task of Multi-modal Retrieval Augmented Multi-modal Generation (M$2$RAG) This task requires foundation models to browse multi-modal web pages, with mixed text and images, and generate multi-modal responses for solving user queries. We construct a benchmark for M$2$RAG task, equipped with a suite of text-modal metrics and multi-modal metrics to analyze the capabilities of existing foundation models.
  arXiv  Detail & Related papers  (2024-11-25T13:20:19Z)
• SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers [20.273439120429025]
  SciDQA is a new dataset for reading comprehension that challenges LLMs for a deep understanding of scientific articles. Unlike other scientific QA datasets, SciDQA sources questions from peer reviews by domain experts and answers by paper authors. Questions in SciDQA necessitate reasoning across figures, tables, equations, appendices, and supplementary materials.
  arXiv  Detail & Related papers  (2024-11-08T05:28:22Z)
• Benchmarking Large Language Models for Conversational Question Answering in Multi-instructional Documents [61.41316121093604]
  We present InsCoQA, a novel benchmark for evaluating large language models (LLMs) in the context of conversational question answering (CQA) Sourced from extensive, encyclopedia-style instructional content, InsCoQA assesses models on their ability to retrieve, interpret, and accurately summarize procedural guidance from multiple documents. We also propose InsEval, an LLM-assisted evaluator that measures the integrity and accuracy of generated responses and procedural instructions.
  arXiv  Detail & Related papers  (2024-10-01T09:10:00Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers [43.18330795060871]
  SPIQA is a dataset specifically designed to interpret complex figures and tables within the context of scientific research articles. We employ automatic and manual curation to create the dataset. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits.
  arXiv  Detail & Related papers  (2024-07-12T16:37:59Z)
• DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models [63.466265039007816]
  We present DocGenome, a structured document benchmark constructed by annotating 500K scientific documents from 153 disciplines in the arXiv open-access community. We conduct extensive experiments to demonstrate the advantages of DocGenome and objectively evaluate the performance of large models on our benchmark.
  arXiv  Detail & Related papers  (2024-06-17T15:13:52Z)
• ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents [21.17856299966841]
  This study introduces ResearchArena, a benchmark designed to evaluate large language models (LLMs) in conducting academic surveys. To support these opportunities, we construct an environment of 12M full-text academic papers and 7.9K survey papers.
  arXiv  Detail & Related papers  (2024-06-13T03:26:30Z)
• Needle In A Multimodal Haystack [79.81804334634408]
  We present the first benchmark specifically designed to evaluate the capability of existing MLLMs to comprehend long multimodal documents. Our benchmark includes three types of evaluation tasks: multimodal retrieval, counting, and reasoning. We observe that existing models still have significant room for improvement on these tasks, especially on vision-centric evaluation.
  arXiv  Detail & Related papers  (2024-06-11T13:09:16Z)
• MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks [56.60050181186531]
  We introduce MM-BigBench, which incorporates a diverse range of metrics to offer an extensive evaluation of the performance of various models and instructions. Our paper evaluates a total of 20 language models (14 MLLMs) on 14 multimodal datasets spanning 6 tasks, with 10 instructions for each task, and derives novel insights.
  arXiv  Detail & Related papers  (2023-10-13T11:57:04Z)
• UniDoc: A Universal Large Multimodal Model for Simultaneous Text Detection, Recognition, Spotting and Understanding [93.92313947913831]
  We introduce UniDoc, a novel multimodal model equipped with text detection and recognition capabilities. To the best of our knowledge, this is the first large multimodal model capable of simultaneous text detection, recognition, spotting, and understanding.
  arXiv  Detail & Related papers  (2023-08-19T17:32:34Z)

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.