We lifted 3 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ckyang1124/sakura | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| extract_judgement | Ran | ckyang1124/sakura/evaluation/llm_judge.py pointer only (licence: NONE) · get_code("99c0f4e50a906542") |
| query_llm | Ran | ckyang1124/sakura/evaluation/llm_judge.py pointer only (licence: NONE) · get_code("4c6c9926d43d2ecf") |
| read_json_file | Ran | ckyang1124/sakura/evaluation/llm_judge.py pointer only (licence: NONE) · get_code("5a7528f5f02fdae8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2505.13237")
get_code_for_paper("2505.13237")
have("2505.13237")
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