1 citations · 1 across the 7 of their papers we have counts for
9 papers
Change the Number, Change the Answer: Assessing Rating Scale Sensitivity in LLM-Based Text Scoring
Wonjeong Jo, Gyuwan Kim, Ming Shan Hee
Large language models (LLMs) are increasingly used in computational social science to assign scalar scores to social and psychological constructs. However, studies use different ra…
CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG
Gyuwan Kim, Cheoneum Park, Tao Yang
Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved contexts for higher efficiency, whi…
Global Optimization and Inference-Time Region Grafting for Agentic Workflows
Donghyeok Koh, Gyuwan Kim, Jinyeong Bak +4
Recent advances in agentic workflow optimization automate workflow design through task-specific workflow search or input-conditioned architecture selection. However, they determine…
KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem
Seongjin Cha, Gyuwan Kim, Dongsu Han +2
Self-speculative decoding (SSD) accelerates LLM inference by skipping layers to create an efficient draft model, yet existing methods often rely on static heuristics that ignore th…
PPA-Plan: Proactive Pitfall Avoidance for Reliable Planning in Long-Context LLM Reasoning
Byeongjin Kim, Gyuwan Kim, Seo Yeon Park
Large language models (LLMs) struggle with reasoning over long contexts where relevant information is sparsely distributed. Although plan-and-execute frameworks mitigate this by de…
MacRAG: Compress, Slice, and Scale-up for Multi-Scale Adaptive Context RAG
Woosang Lim, Zekun Li, Gyuwan Kim +6
Long-context large language models (LC LLMs) combined with retrieval-augmented generation (RAG) hold strong potential for complex multi-hop and large-document tasks. However, exist…
AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
Soyoung Yoon, Gyuwan Kim, Gyu-Hwung Cho +1
Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications. Due to the limit in context size and high inference cost of long c…
ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contexts
Dongwon Noh, Donghyeok Koh, Junghun Yuk +4
Prior benchmarks for evaluating the domain-specific knowledge of large language models (LLMs) lack the scalability to handle complex academic tasks. To address this, we introduce \…
Detecting Training Data of Large Language Models via Expectation Maximization
Gyuwan Kim, Yang Li, Evangelia Spiliopoulou +2
Membership inference attacks (MIAs) aim to determine whether a specific example was used to train a given language model. While prior work has explored prompt-based attacks such as…