1 citations · 1 across the 4 of their papers we have counts for
4 papers
Don't Lose Entities from Retrieval to Generation: Dual Entity Recovery RAG for multi-hop QA
Heechang Lee, Dong-Young Lim
Retrieval-augmented multi-hop question answering (QA) decomposes a query into sub-questions and decomposes the corpus into smaller retrieval units such as sentences. Both forms of…
TIDES: A Longitudinal Bilingual Dataset for Modeling Multi-Party Social Dynamics
Heechan Lee, Jeonggyu Kang, Junho Myung +3
Group conversations are fundamental to human collaboration, yet standard large language models (LLMs) still struggle with the complexities of multi-party interaction. This challeng…
Evalet: Evaluating Large Language Models through Functional Fragmentation
Tae Soo Kim, Heechan Lee, Yoonjoo Lee +2
Practitioners increasingly rely on Large Language Models (LLMs) to evaluate generative AI outputs through "LLM-as-a-Judge" approaches. However, these methods produce holistic score…
DGSAM: Domain Generalization via Individual Sharpness-Aware Minimization
Youngjun Song, Youngsik Hwang, Jonghun Lee +2
Domain generalization (DG) aims to learn models that perform well on unseen target domains by training on multiple source domains. Sharpness-Aware Minimization (SAM), known for fin…