Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2503.06899 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866918021559222272 |
|---|---|
| author | Shi, Xiaoming Liu, Zeming Lei, Yiming Zhang, Chenkai Leng, Haitao Wang, Chuan Liu, Qingjie Che, Wanxiang Liu, Shaoguo Li, Size Wang, Yunhong |
| author_facet | Shi, Xiaoming Liu, Zeming Lei, Yiming Zhang, Chenkai Leng, Haitao Wang, Chuan Liu, Qingjie Che, Wanxiang Liu, Shaoguo Li, Size Wang, Yunhong |
| contents | Video-based dialogue systems, such as education assistants, have compelling application value, thereby garnering growing interest. However, the current video-based dialogue systems are limited by their reliance on a single dialogue type, which hinders their versatility in practical applications across a range of scenarios, including question-answering, emotional dialog, etc. In this paper, we identify this challenge as how to generate video-driven multilingual mixed-type dialogues. To mitigate this challenge, we propose a novel task and create a human-to-human video-driven multilingual mixed-type dialogue corpus, termed KwaiChat, containing a total of 93,209 videos and 246,080 dialogues, across 4 dialogue types, 30 domains, 4 languages, and 13 topics. Additionally, we establish baseline models on KwaiChat. An extensive analysis of 7 distinct LLMs on KwaiChat reveals that GPT-4o achieves the best performance but still cannot perform well in this situation even with the help of in-context learning and fine-tuning, which indicates that the task is not trivial and needs further research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06899 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus Shi, Xiaoming Liu, Zeming Lei, Yiming Zhang, Chenkai Leng, Haitao Wang, Chuan Liu, Qingjie Che, Wanxiang Liu, Shaoguo Li, Size Wang, Yunhong Computation and Language Video-based dialogue systems, such as education assistants, have compelling application value, thereby garnering growing interest. However, the current video-based dialogue systems are limited by their reliance on a single dialogue type, which hinders their versatility in practical applications across a range of scenarios, including question-answering, emotional dialog, etc. In this paper, we identify this challenge as how to generate video-driven multilingual mixed-type dialogues. To mitigate this challenge, we propose a novel task and create a human-to-human video-driven multilingual mixed-type dialogue corpus, termed KwaiChat, containing a total of 93,209 videos and 246,080 dialogues, across 4 dialogue types, 30 domains, 4 languages, and 13 topics. Additionally, we establish baseline models on KwaiChat. An extensive analysis of 7 distinct LLMs on KwaiChat reveals that GPT-4o achieves the best performance but still cannot perform well in this situation even with the help of in-context learning and fine-tuning, which indicates that the task is not trivial and needs further research. |
| title | KwaiChat: A Large-Scale Video-Driven Multilingual Mixed-Type Dialogue Corpus |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.06899 |