Summary of Diasynth: Synthetic Dialogue Generation Framework For Low Resource Dialogue Applications, by Sathya Krishnan Suresh et al.
DiaSynth: Synthetic Dialogue Generation Framework for Low Resource Dialogue Applications
by Sathya Krishnan Suresh, Wu Mengjun, Tushar Pranav, Eng Siong Chng
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 abstract introduces DiaSynth, a synthetic dialogue generation framework that addresses the scarcity of domain-specific datasets for training dialogue systems. Unlike existing frameworks, DiaSynth uses Large Language Models (LLMs) and Chain of Thought (CoT) reasoning to generate high-quality dialogues across various domains. The authors perform experiments using different LLMs and few-shot examples from DialogSum and SAMSum, fine-tuning the models on synthetic data that outperforms base models by 16.47% on dialogue summarization. The results demonstrate DiaSynth’s potential as a robust alternative to traditional data collection methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DiaSynth is a new way to make computers talk like humans. Right now, we don’t have enough special-purpose datasets for training these computer conversations. Existing research uses general or very specific datasets that aren’t big enough to train good conversation systems. DiaSynth changes this by creating fake conversations using really smart language models and clever thinking about what people might say in different situations. The results show that these fake conversations are actually pretty good, and can even be better than real data collected from humans. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Summarization » Synthetic data
