SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
<|reference_start|>SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking: In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt, requiring access to labeled training data. Procuring such training data for a wide range of domains and applications is time-consuming, expensive, and, at times, infeasible. While zero-shot learning requires no training data, it significantly lags behind the few-shot setup. Thus, `\textit{Can we efficiently generate synthetic data for any dialogue schema to enable few-shot prompting?}' Addressing this question, we propose \method, a data generation framework tailored for DST, utilizing LLMs. Our approach only requires the dialogue schema and a few hand-crafted dialogue templates to synthesize natural, coherent, and free-flowing dialogues with DST annotations. Few-shot learning using data from {\method} results in $4-5%$ improvement in Joint Goal Accuracy over the zero-shot baseline on MultiWOZ 2.1 and 2.4. Remarkably, our few-shot learning approach recovers nearly $98%$ of the performance compared to the few-shot setup using human-annotated training data. Our synthetic data and code can be accessed at https://github.com/apple/ml-synthdst<|reference_end|>
@article{kulkarni2024synthdst:,
    title={SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State
  Tracking},
    author={Atharva Kulkarni, Bo-Hsiang Tseng, Joel Ruben Antony Moniz, Dhivya
  Piraviperumal, Hong Yu, Shruti Bhargava},
    journal={arXiv preprint arXiv:2402.02285},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02285},
    primaryClass={cs.CL cs.AI cs.LG}
}