1 citations · 1 across the 5 of their papers we have counts for
14 papers · 1 filter
From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
Youngjoon Jang, Seongtae Hong, Junyoung Son +3
Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrati…
LANGSAE EDITING: Improving Multilingual Information Retrieval via Post-hoc Language Identity Removal
Dongjun Kim, Jeongho Yoon, Chanjun Park +1
Dense retrieval in multilingual settings often searches over mixed-language collections, yet multilingual embeddings encode language identity alongside semantics. This language sig…
KITE: A Benchmark for Evaluating Korean Instruction-Following Abilities in Large Language Models
Dongjun Kim, Chanhee Park, Chanjun Park +1
The instruction-following capabilities of large language models (LLMs) are pivotal for numerous applications, from conversational agents to complex reasoning systems. However, curr…
Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks
Dongjun Kim, Gyuho Shim, Yongchan Chun +3
Large Language Models are commonly judged by their scores on standard benchmarks, yet such scores often overstate real capability since they mask the mix of skills a task actually…
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval
Yongchan Chun, Minhyuk Kim, Dongjun Kim +2
Automatic Term Extraction (ATE) identifies domain-specific expressions that are crucial for downstream tasks such as machine translation and information retrieval. Although large l…
MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation
Chanhee Park, Hyeonseok Moon, Chanjun Park +1
Retrieval-Augmented Generation (RAG) has gained prominence as an effective method for enhancing the generative capabilities of Large Language Models (LLMs) through the incorporatio…