1 citations · 1 across the 9 of their papers we have counts for
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Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization
Dong-Jae Koh, Huisu Kim, SeongHwan Yoon +4
Large Language Models (LLMs) are increasingly used to generate structured outputs, but their reliability remains unclear when those outputs must satisfy database-level constraints.…
From What to Respond to When to Respond: Timely Response Generation for Open-domain Dialogue Agents
Seongbo Jang, Minjin Jeon, Jaehoon Lee +3
While research on dialogue response generation has primarily focused on generating coherent responses conditioning on textual context, the critical question of when to respond grou…
On the Effectiveness of Integration Methods for Multimodal Dialogue Response Retrieval
Seongbo Jang, Seonghyeon Lee, Dongha Lee +1
Multimodal chatbots have become one of the major topics for dialogue systems in both research community and industry. Recently, researchers have shed light on the multimodality of…
Verbosity-Aware Rationale Reduction: Effective Reduction of Redundant Rationale via Principled Criteria
Joonwon Jang, Jaehee Kim, Wonbin Kweon +2
Large Language Models (LLMs) rely on generating extensive intermediate reasoning units (e.g., tokens, sentences) to enhance final answer quality across a wide range of complex task…
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark
Seongbo Jang, Seonghyeon Lee, Hwanjo Yu
As language models are often deployed as chatbot assistants, it becomes a virtue for models to engage in conversations in a user's first language. While these models are trained on…