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Found 3 papers, 1 papers with code
A Survey on Recent Advances in Conversational Data Generation
Recent advancements in conversational systems have significantly enhanced human-machine interactions across various domains.
Efficient data selection employing Semantic Similarity-based Graph Structures for model training
The results indicate reliable projections of ASR performance, with a 93% accuracy increase when using the proposed method compared to random predictions, bringing non-trivial information on the impact of textual representations in speech models.
Self-seeding and Multi-intent Self-instructing LLMs for Generating Intent-aware Information-Seeking dialogs
Furthermore, we propose SOLID-RL, which is further trained to generate a dialog in one step on the data generated by SOLID.
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