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Notes on dialogue systems
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Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations
The research presents a new framework called Preference Tree Optimization (PTO) for enhancing dialogue systems in specialized domains with limited data. It focuses on Motivational Interviewing (MI), a counseling…
Preference Tree Optimization: Enhancing Goal-Oriented Dialogue with Look-Ahead Simulations
Researchers propose Preference Tree Optimization (PTO), a framework that enhances dialogue systems in specialized domains like Motivational Interviewing by using preference data generated through Look-Ahead Simulations…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Researchers have proposed a new type of multimodal agent called the Cognitive-structured Multimodal Agent. This agent is designed to improve long-horizon multimodal dialogue by externalizing visual information into an…
Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment
Researchers evaluate the accuracy and quality of conversations between developers and a Language Model (LLM)-based agent in the context of Health Insurance Portability and Accountability Act (HIPAA) regulatory…
Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment
Researchers examined the accuracy of Large Language Model (LLM)-based dialogue assistants in handling Non-Functional Requirements (NFRs) related to Health Insurance Portability and Accountability Act (HIPAA) regulatory…
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