1 citations · 1 across the 9 of their papers we have counts for
10 papers
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.…
Layer-wise Target Propagation: Efficient Component Attribution through Target Centric Propagation
Lasse Marten Jantsch, Dong-Jae Koh, Seonghyeon Lee +1
Understanding the internal mechanisms of transformer-based large language models (LLMs) is crucial for their reliable deployment and effective operation. While recent efforts have…
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…
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
Seonghyeon Lee, Heejae Chon, Joonwon Jang +2
Language models (LMs) have exhibited impressive abilities in generating code from natural language requirements. In this work, we highlight the diversity of code generated by LMs a…
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…
Eliciting Instruction-tuned Code Language Models' Capabilities to Utilize Auxiliary Function for Code Generation
Seonghyeon Lee, Suyeon Kim, Joonwon Jang +3
We study the code generation behavior of instruction-tuned models built on top of code pre-trained language models when they could access an auxiliary function to implement a funct…
Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes
Heejae Chon, Seonghyeon Lee, Jinyoung Yeo +1
Language models (LMs) have exhibited impressive abilities in generating codes from natural language requirements. In this work, we highlight the diversity of code generated by LMs…
Exploring Language Model's Code Generation Ability with Auxiliary Functions
Seonghyeon Lee, Sanghwan Jang, Seongbo Jang +2
Auxiliary function is a helpful component to improve language model's code generation ability. However, a systematic exploration of how they affect has yet to be done. In this work…
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…