Summary of Benchmarking Open-ended Audio Dialogue Understanding For Large Audio-language Models, by Kuofeng Gao et al.
Benchmarking Open-ended Audio Dialogue Understanding for Large Audio-Language Models
by Kuofeng Gao, Shu-Tao Xia, Ke Xu, Philip Torr, Jindong Gu
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Audio Dialogue Understanding Benchmark (ADU-Bench) aims to evaluate the performance of Large Audio-Language Models (LALMs) in open-ended audio dialogues. The benchmark consists of four datasets assessing LALMs’ abilities across various scenarios, skills, languages, and ambiguity handling categories. Notably, it evaluates ambiguity handling in audio dialogues with different intentions beyond literal sentence meanings. For instance, “Really!?” can have varying intonations conveying distinct intentions. Existing LALMs struggle with mathematical symbols, understanding human behavior like roleplay, comprehending multiple languages, and handling ambiguities from phonetic elements such as intonation, pause positions, and homophones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Audio-Language Models (LALMs) are getting better at talking to humans! They can have conversations back-and-forth with us. This is cool because it means they can help in lots of real-life situations where people talk to each other. But, we need a special test to see how well LALMs do this. That’s what the Audio Dialogue Understanding Benchmark (ADU-Bench) is for. It has many examples of conversations that are open-ended, meaning there’s no right or wrong answer. The test looks at things like how well LALMs understand different languages, and how they handle tricky situations where people might say the same thing but mean different things. |
