title: SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based
    on Speech and Audio Information
summary: Large audio-language models (LALMs) extend the large language models with
  multimodal understanding in speech, audio, etc. While their performances on
  speech and audio-processing tasks are extensively studied, their reasoning
  abilities remain underexplored. Particularly, their multi-hop reasoning, the
  ability to recall and integrate multiple facts, lacks systematic evaluation.
  Existing benchmarks focus on general speech and audio-processing tasks,
  conversational abilities, and fairness but overlook this aspect. To bridge this
  gap, we introduce SAKURA, a benchmark assessing LALMs' multi-hop reasoning
  based on speech and audio information. Results show that LALMs struggle to
  integrate speech/audio representations for multi-hop reasoning, even when they
  extract the relevant information correctly, highlighting a fundamental
  challenge in multimodal reasoning. Our findings expose a critical limitation in
  LALMs, offering insights and resources for future research.
paper_authors._id: 6830741133a25ec4e8a40563
paper_authors.name: Chih-Kai Yang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-05-23T13:21:30.080Z
paper_authors._id: 646fa3016441111304fec68d
paper_authors.avatarUrl: /avatars/923629340f3785ae8c6e52cf3674d5c2.svg
paper_authors.fullname: Chih-Kai Yang
paper_authors.type: user
paper_authors.user: zenyn
paper_authors._id: 6830741133a25ec4e8a40564
paper_authors.name: Neo Ho
paper_authors._id: 6830741133a25ec4e8a40565
paper_authors.name: Yen-Ting Piao
paper_authors._id: 6830741133a25ec4e8a40566
paper_authors.name: Hung-yi Lee
paper_title: SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based
    on Speech and Audio Information
paper_summary: Large audio-language models (LALMs) extend the large language models with
  multimodal understanding in speech, audio, etc. While their performances on
  speech and audio-processing tasks are extensively studied, their reasoning
  abilities remain underexplored. Particularly, their multi-hop reasoning, the
  ability to recall and integrate multiple facts, lacks systematic evaluation.
  Existing benchmarks focus on general speech and audio-processing tasks,
  conversational abilities, and fairness but overlook this aspect. To bridge this
  gap, we introduce SAKURA, a benchmark assessing LALMs' multi-hop reasoning
  based on speech and audio information. Results show that LALMs struggle to
  integrate speech/audio representations for multi-hop reasoning, even when they
  extract the relevant information correctly, highlighting a fundamental
  challenge in multimodal reasoning. Our findings expose a critical limitation in
  LALMs, offering insights and resources for future research.
paper_ai_summary: SAKURA is introduced to evaluate the multi-hop reasoning abilities of large audio-language models, revealing their struggles in integrating speech/audio representations.
paper_ai_keywords: large audio-language models multimodal understanding speech audio multi-hop reasoning SAKURA