Active sample selection over review, metadata, and collaborative seed data plus LLM-generated synthetic dialogues improves fine-tuned conversational recommendation on ReDial and INSPIRED, though not uniformly across all settings.
DG2: Data Augmentation Through Document Grounded Dialogue Generation
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abstract
Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation. Especially in document-grounded dialog systems, human experts need to carefully read the unstructured documents to answer the users' questions. As a result, existing document-grounded dialog datasets are relatively small-scale and obstruct the effective training of dialogue systems. In this paper, we propose an automatic data augmentation technique grounded on documents through a generative dialogue model. The dialogue model consists of a user bot and agent bot that can synthesize diverse dialogues given an input document, which are then used to train a downstream model. When supplementing the original dataset, our method achieves significant improvement over traditional data augmentation methods. We also achieve great performance in the low-resource setting.
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2025 1verdicts
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From Reviews to Dialogues: Active Synthesis for Zero-Shot LLM-based Conversational Recommender System
Active sample selection over review, metadata, and collaborative seed data plus LLM-generated synthetic dialogues improves fine-tuned conversational recommendation on ReDial and INSPIRED, though not uniformly across all settings.