Modeling Real-Time Interactive Conversations as Timed Diarized Transcripts
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909208184619008 |
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| author | Tanzer, Garrett Ahdritz, Gustaf Melas-Kyriazi, Luke |
| author_facet | Tanzer, Garrett Ahdritz, Gustaf Melas-Kyriazi, Luke |
| contents | Chatbots built upon language models have exploded in popularity, but they have largely been limited to synchronous, turn-by-turn dialogues. In this paper we present a simple yet general method to simulate real-time interactive conversations using pretrained text-only language models, by modeling timed diarized transcripts and decoding them with causal rejection sampling. We demonstrate the promise of this method with two case studies: instant messenger dialogues and spoken conversations, which require generation at about 30 tok/s and 20 tok/s respectively to maintain real-time interactivity. These capabilities can be added into language models using relatively little data and run on commodity hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13203 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling Real-Time Interactive Conversations as Timed Diarized Transcripts Tanzer, Garrett Ahdritz, Gustaf Melas-Kyriazi, Luke Machine Learning Computation and Language Chatbots built upon language models have exploded in popularity, but they have largely been limited to synchronous, turn-by-turn dialogues. In this paper we present a simple yet general method to simulate real-time interactive conversations using pretrained text-only language models, by modeling timed diarized transcripts and decoding them with causal rejection sampling. We demonstrate the promise of this method with two case studies: instant messenger dialogues and spoken conversations, which require generation at about 30 tok/s and 20 tok/s respectively to maintain real-time interactivity. These capabilities can be added into language models using relatively little data and run on commodity hardware. |
| title | Modeling Real-Time Interactive Conversations as Timed Diarized Transcripts |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2405.13203 |