Multi-Document Grounded Multi-Turn Synthetic Dialog Generation

Abstract: We introduce a technique for multi-document grounded multi-turn synthetic dialog generation that incorporates three main ideas. First, we control the overall dialog flow using taxonomy-driven user queries that are generated with Chain-of-Thought (CoT) prompting. Second, we support the generation of multi-document grounded dialogs by mimicking real-world use of retrievers to update the grounding documents after every user-turn in the dialog. Third, we apply LLM-as-a-Judge to filter out queries with incorrect answers. Human evaluation of the synthetic dialog data suggests that the data is diverse, coherent, and includes mostly correct answers. Both human and automatic evaluations of answerable queries indicate that models fine-tuned on synthetic dialogs consistently out-perform those fine-tuned on existing human generated training data across four publicly available multi-turn document grounded benchmark test sets.

• The paper introduces a sophisticated method for generating high-quality, synthetic multi-document grounded, multi-turn dialogs using taxonomy-driven flow, multi-document grounding, and LLM-as-a-Judge filtering.
• Empirical evaluations show the generated dialogs are high-quality, coherent, and diverse, with models trained on this synthetic data outperforming those using human data on four benchmarks for answerable queries.
• This research demonstrates the potential of using synthetic data to train complex dialog systems, offering significant cost and time savings compared to relying solely on scarce human-annotated datasets.

Multi-Document Grounded Multi-Turn Synthetic Dialog Generation

The paper introduces a sophisticated approach to generate synthetic dialogs that are both multi-document grounded and multi-turn. This method incorporates curated strategies to craft dialog flows that mirror real-world scenarios where information retrieval is imperative. This paper's insights can potentially elevate the authenticity and applicability of synthetic data in training dialog systems.

Core Methodologies

The paper devises a framework using three core methodologies:

1. Taxonomy-Driven Dialog Flow: The dialogs are orchestrated using a taxonomy-driven approach whereby user queries are meticulously generated using chain-of-thought prompting (CoT). This ensures that the queries are contextual and diverse, elements necessary for maintaining a realistic dialog flow.
2. Multi-Document Grounding: The generation process adapts to new information by actively updating the set of grounding documents after each user utterance. This emulates the dynamic nature of real-world dialogues where responses often rely on iterative information retrieval processes. Unlike single-document grounding, this approach can integrate insights from several documents, thereby enriching the dialog content.
3. LLM-as-a-Judge Mechanism: The incorporation of a LLM as an adjudicator helps in filtering out conversations laden with inaccuracies. This ensures the overall quality and reliability of the generated dialog, maintaining its informative and educational purposes.

Evaluation and Findings

Empirical evaluations involving human assessments emphasize the high-quality nature of the generated dialogs, noting their diversity and coherence. Notably, in answerable queries, models fine-tuned with this synthetic data outperform those trained on existing human-generated dialog sets across four benchmarks, indicating the efficacy of synthetic data in training models for complex dialog systems.

Implications and Future Prospects

This research underscores the potential of utilizing synthetic data for tasks traditionally reliant on scarce human-annotated datasets. As AI models become more adept at simulating and learning from synthetic dialogs, this direction offers substantial reductions in human annotation costs and time. The implications extend to various applications, from virtual assistants to customer service bots, exemplifying the adaptability and depth that multi-document synthetic dialog systems can achieve.

Looking forward, future developments may focus on enhancing the dialog generation pipeline with even more intricate retrieval mechanisms and expanding the application scope to include unanswerable and adversarial query contexts. With the inherent ability to simulate real-world environments more accurately, advancements in multi-turn dialog generation could further enhance the robustness and performance of future AI systems.