title: M^3FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial
    Meeting Understanding Evaluation Dataset
summary: Recent breakthroughs in large language models (LLMs) have led to the
  development of new benchmarks for evaluating their performance in the financial
  domain. However, current financial benchmarks often rely on news articles,
  earnings reports, or announcements, making it challenging to capture the
  real-world dynamics of financial meetings. To address this gap, we propose a
  novel benchmark called M^3FinMeeting, which is a multilingual,
  multi-sector, and multi-task dataset designed for financial meeting
  understanding. First, M^3FinMeeting supports English, Chinese, and
  Japanese, enhancing comprehension of financial discussions in diverse
  linguistic contexts. Second, it encompasses various industry sectors defined by
  the Global Industry Classification Standard (GICS), ensuring that the benchmark
  spans a broad range of financial activities. Finally,
  M^3FinMeeting includes three tasks: summarization, question-answer
  (QA) pair extraction, and question answering, facilitating a more realistic and
  comprehensive evaluation of understanding. Experimental results with seven
  popular LLMs reveal that even the most advanced long-context models have
  significant room for improvement, demonstrating the effectiveness of
  M^3FinMeeting as a benchmark for assessing LLMs' financial meeting
  comprehension skills.
paper_authors._id: 683faa31f0564d1fb4b9ffc6
paper_authors.name: Jie Zhu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T08:57:01.885Z
paper_authors._id: 642656cbad1e3b0e6e91b752
paper_authors.avatarUrl: /avatars/3bf0ee15fd528e09b2b889f5cce3cbd0.svg
paper_authors.fullname: Jie Zhu
paper_authors.type: user
paper_authors.user: amazingj
paper_authors._id: 683faa31f0564d1fb4b9ffc7
paper_authors.name: Junhui Li
paper_authors._id: 683faa31f0564d1fb4b9ffc8
paper_authors.name: Yalong Wen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:38:27.545Z
paper_authors._id: 64be2b4fc733e8552fea3fab
paper_authors.avatarUrl: /avatars/661efe278fe5885715c8fe27129d285b.svg
paper_authors.fullname: yalong wen
paper_authors.type: user
paper_authors.user: yalongwen
paper_authors._id: 683faa31f0564d1fb4b9ffc9
paper_authors.name: Xiandong Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:38:34.119Z
paper_authors._id: 68382360c537d91527480f2f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/WGDHKjEdsWZp9nA77Zehc.png
paper_authors.fullname: Xiandong Li
paper_authors.type: user
paper_authors.user: Diracket
paper_authors._id: 683faa31f0564d1fb4b9ffca
paper_authors.name: Lifan Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:38:20.620Z
paper_authors._id: 62f0bb7f47cf7b3344fe9ad6
paper_authors.avatarUrl: /avatars/e509f31af551cfd30e319b03d4c92740.svg
paper_authors.fullname: guo
paper_authors.type: user
paper_authors.user: lifan
paper_authors._id: 683faa31f0564d1fb4b9ffcb
paper_authors.name: Feng Chen
paper_title: M^3FinMeeting: A Multilingual, Multi-Sector, and Multi-Task Financial
    Meeting Understanding Evaluation Dataset
paper_summary: Recent breakthroughs in large language models (LLMs) have led to the
  development of new benchmarks for evaluating their performance in the financial
  domain. However, current financial benchmarks often rely on news articles,
  earnings reports, or announcements, making it challenging to capture the
  real-world dynamics of financial meetings. To address this gap, we propose a
  novel benchmark called M^3FinMeeting, which is a multilingual,
  multi-sector, and multi-task dataset designed for financial meeting
  understanding. First, M^3FinMeeting supports English, Chinese, and
  Japanese, enhancing comprehension of financial discussions in diverse
  linguistic contexts. Second, it encompasses various industry sectors defined by
  the Global Industry Classification Standard (GICS), ensuring that the benchmark
  spans a broad range of financial activities. Finally,
  M^3FinMeeting includes three tasks: summarization, question-answer
  (QA) pair extraction, and question answering, facilitating a more realistic and
  comprehensive evaluation of understanding. Experimental results with seven
  popular LLMs reveal that even the most advanced long-context models have
  significant room for improvement, demonstrating the effectiveness of
  M^3FinMeeting as a benchmark for assessing LLMs' financial meeting
  comprehension skills.
paper_ai_summary: A new multilingual, multi-sector, and multi-task benchmark, M³FinMeeting, evaluates large language models' performance in understanding financial meetings across different languages and industries.
paper_ai_keywords: large language models multilingual multi-sector multi-task benchmark financial meeting understanding Global Industry Classification Standard (GICS) summarization question-answer pair extraction question answering