DiaSynth -- Synthetic Dialogue Generation Framework
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.
66fb8de3165fd0b0a7c4569c
Sathya Krishnan Suresh
claimed_verified
2024-10-01T07:16:38.689Z
62f0e457bc8201db9ef47f89
https://cdn-avatars.huggingface.co/v1/production/uploads/62f0e457bc8201db9ef47f89/zOhDptwZpDGaugKCBOWB2.jpeg
Sathya Krishnan
user
SkAndMl
66fb8de3165fd0b0a7c4569d
Wu Mengjun
admin_assigned
2024-10-01T08:15:37.351Z
66fb9345e27f993856b1f3f8
/avatars/3bd4b5bb13f6718c627439c07bfbd564.svg
Wu Mengjun
user
AlexWuuuu
66fb8de3165fd0b0a7c4569e
Tushar Pranav
66fb8de3165fd0b0a7c4569f
Eng Siong Chng
DiaSynth -- Synthetic Dialogue Generation Framework
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.
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.
synthetic dialogue generation Large Language Model (LLM) Chain of Thought (CoT) simulated personas subtopics conversational characteristics in-domain data few-shot examples DialogSum SAMSum