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Found 7 papers, 2 papers with code
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.
RLAIF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences.
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation.
AnyTOD: A Programmable Task-Oriented Dialog System
We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training.
Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.
Description-Driven Task-Oriented Dialog Modeling
Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks.
SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems
Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research.
