2024/05/24 by Vishal Vivek Saley, Rocktim Jyoti Das, Saley, Vishal Vivek +5 · 2 citations
Computer Science · #Computation and Language (cs.CL) #Context-Aware Activity Recognition Systems #FOS: Computer and information sciences #Multi-Agent Systems and Negotiation #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.2405.15585
openalex publication_date 2024/05/24 · openalex created_date 2024/05/28 · openalex updated_date 2026/07/28
End-to-end Task-Oriented Dialog (TOD) systems typically require extensive training datasets to perform well. In contrast, large language model (LLM) based TOD systems can excel even with limited data due to their ability to learn tasks through in-context exemplars. However, these models lack alignment with the style of responses in training data and often generate comprehensive responses, making it difficult for users to grasp the information quickly. In response, we propose SyncTOD that synergizes LLMs with task-specific hints to improve alignment in low-data settings. SyncTOD employs small auxiliary models to provide hints and select exemplars for in-context prompts. With ChatGPT, SyncTOD achieves superior performance compared to LLM-based baselines and SoTA models in low-data settings, while retaining competitive performance in full-data settings.