title: DiaSynth -- Synthetic Dialogue Generation Framework
summary: The scarcity of domain specific dialogue datasets across various domains,
  from academic topics to everyday conversations, limits the development of
  dialogue systems for various applications. Existing research is often
  constrained either by dialogue datasets that are too general or by niche domain
  dialogue datasets whose scale does not match the required scale for training
  dialogue systems. To address this gap, we introduce DiaSynth - a synthetic
  dialogue generation framework capable of generating high quality, contextually
  rich dialogues across a wide range of domains. Our approach differs from
  existing frameworks by dynamically generating dialogues that incorporate
  simulated personas, subtopics, and diverse conversational characteristics,
  using a Large Language Model (LLM) with Chain of Thought (CoT) reasoning to
  create contextually rich, domain-specific dialogues that closely mimic natural
  human interactions. DiaSynth produces tailored dialogues that emulate realistic
  conversations. We perform our experiments by generating synthetic data using
  different LLMs and few-shot examples from DialogSum and SAMSum. The pretrained
  language models fine-tuned on the synthetic data outperform the base models by
  16.47%, while the comparison between models fine-tuned on in-domain data and
  synthetic data shows that the synthetic data is able to capture 90.48% of the
  distribution of the in-domain data. The quality of the data generated also
  scales with the size of LLMs. These results validate DiaSynth's potential as a
  robust alternative to traditional data collection methods.
paper_authors._id: 66fb8de3165fd0b0a7c4569c
paper_authors.name: Sathya Krishnan Suresh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-01T07:16:38.689Z
paper_authors._id: 62f0e457bc8201db9ef47f89
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62f0e457bc8201db9ef47f89/zOhDptwZpDGaugKCBOWB2.jpeg
paper_authors.fullname: Sathya Krishnan
paper_authors.type: user
paper_authors.user: SkAndMl
paper_authors._id: 66fb8de3165fd0b0a7c4569d
paper_authors.name: Wu Mengjun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T08:15:37.351Z
paper_authors._id: 66fb9345e27f993856b1f3f8
paper_authors.avatarUrl: /avatars/3bd4b5bb13f6718c627439c07bfbd564.svg
paper_authors.fullname: Wu Mengjun
paper_authors.type: user
paper_authors.user: AlexWuuuu
paper_authors._id: 66fb8de3165fd0b0a7c4569e
paper_authors.name: Tushar Pranav
paper_authors._id: 66fb8de3165fd0b0a7c4569f
paper_authors.name: Eng Siong Chng
paper_title: DiaSynth -- Synthetic Dialogue Generation Framework
paper_summary: The scarcity of domain specific dialogue datasets across various domains,
  from academic topics to everyday conversations, limits the development of
  dialogue systems for various applications. Existing research is often
  constrained either by dialogue datasets that are too general or by niche domain
  dialogue datasets whose scale does not match the required scale for training
  dialogue systems. To address this gap, we introduce DiaSynth - a synthetic
  dialogue generation framework capable of generating high quality, contextually
  rich dialogues across a wide range of domains. Our approach differs from
  existing frameworks by dynamically generating dialogues that incorporate
  simulated personas, subtopics, and diverse conversational characteristics,
  using a Large Language Model (LLM) with Chain of Thought (CoT) reasoning to
  create contextually rich, domain-specific dialogues that closely mimic natural
  human interactions. DiaSynth produces tailored dialogues that emulate realistic
  conversations. We perform our experiments by generating synthetic data using
  different LLMs and few-shot examples from DialogSum and SAMSum. The pretrained
  language models fine-tuned on the synthetic data outperform the base models by
  16.47%, while the comparison between models fine-tuned on in-domain data and
  synthetic data shows that the synthetic data is able to capture 90.48% of the
  distribution of the in-domain data. The quality of the data generated also
  scales with the size of LLMs. These results validate DiaSynth's potential as a
  robust alternative to traditional data collection methods.
paper_ai_summary: DiaSynth generates high-quality, contextually rich, and domain-specific synthetic dialogues using a Large Language Model with Chain of Thought reasoning, demonstrating competitive performance in comparison to in-domain datasets.
paper_ai_keywords: synthetic dialogue generation Large Language Model (LLM) Chain of Thought (CoT) simulated personas subtopics conversational characteristics in-domain data few-shot examples DialogSum SAMSum