2 citations · 2 across the 14 of their papers we have counts for
6 papers · 1 filter
Multi-Facet Blending for Faceted Query-by-Example Retrieval
Heejin Do, Sangwon Ryu, Jonghwi Kim +1
With the growing demand to fit fine-grained user intents, faceted query-by-example (QBE), which retrieves similar documents conditioned on specific facets, has gained recent attent…
Exploring Iterative Controllable Summarization with Large Language Models
Sangwon Ryu, Heejin Do, Daehee Kim +5
Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., leng…
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards
Heejin Do, Sangwon Ryu, Gary Geunbae Lee
Recent advances in automated essay scoring (AES) have shifted towards evaluating multiple traits to provide enriched feedback. Like typical AES systems, multi-trait AES employs the…
Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model
Daehee Kim, Deokhyung Kang, Sangwon Ryu +1
Knowledge Graph-to-Text (G2T) generation involves verbalizing structured knowledge graphs into natural language text. Recent advancements in Pretrained Language Models (PLMs) have…
Key-Element-Informed sLLM Tuning for Document Summarization
Sangwon Ryu, Heejin Do, Yunsu Kim +2
Remarkable advances in large language models (LLMs) have enabled high-quality text summarization. However, this capability is currently accessible only through LLMs of substantial…
Multi-Dimensional Optimization for Text Summarization via Reinforcement Learning
Sangwon Ryu, Heejin Do, Yunsu Kim +2
The evaluation of summary quality encompasses diverse dimensions such as consistency, coherence, relevance, and fluency. However, existing summarization methods often target a spec…