1 citations · 1 across the 8 of their papers we have counts for
8 papers
DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues
Takyoung Kim, Kang-wook Kim, Sang Hoon Woo +3
Turn-taking is a central component of full-duplex interaction. Which turn-taking behaviors are appropriate varies with the scenario, yet current models apply a single norm regardle…
From Documents to Segments: A Contextual Reformulation for Topic Assignment
Hoonsang Yoon, Takyoung Kim, Wonkee Lee +3
Traditional topic modeling assigns a single topic to each document. In practice, however, many real-world documents, such as product reviews or open-ended survey responses, contain…
ReIn: Conversational Error Recovery with Reasoning Inception
Takyoung Kim, Jinseok Nam, Chandrayee Basu +5
Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to una…
Goal Alignment in LLM-Based User Simulators for Conversational AI
Shuhaib Mehri, Xiaocheng Yang, Takyoung Kim +3
User simulators are essential to conversational AI, enabling scalable agent development and evaluation through simulated interactions. While current Large Language Models (LLMs) ha…
AURA: A Diagnostic Framework for Tracking User Satisfaction of Interactive Planning Agents
Takyoung Kim, Janvijay Singh, Shuhaib Mehri +6
The growing capabilities of large language models (LLMs) in instruction-following and context-understanding lead to the era of agents with numerous applications. Among these, task…
TD-EVAL: Revisiting Task-Oriented Dialogue Evaluation by Combining Turn-Level Precision with Dialogue-Level Comparisons
Emre Can Acikgoz, Carl Guo, Suvodip Dey +4
Task-oriented dialogue (TOD) systems are experiencing a revolution driven by Large Language Models (LLMs), yet the evaluation methodologies for these systems remain insufficient fo…
Premise-Augmented Reasoning Chains Improve Error Identification in Math reasoning with LLMs
Sagnik Mukherjee, Abhinav Chinta, Takyoung Kim +2
Chain-of-Thought (CoT) prompting enhances mathematical reasoning in large language models (LLMs) by enabling detailed step-by-step solutions. However, due to the verbosity of LLMs,…
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation
Takyoung Kim, Kyungjae Lee, Young Rok Jang +4
Interactions with large language models (LLMs) often yield long and detailed responses, leveraging both parametric knowledge and retrieval-augmented generation (RAG). While these r…