25 citations · 39 across the 16 of their papers we have counts for
17 papers
LLMs Learn Better In-Context from Rules than from Examples
Xiang Fu, Seungmin Cho, Yukyung Lee +1
Large language models (LLMs) exhibit in-context learning capabilities, where they can learn new tasks from prompt contexts without weight updates. We compare the learning efficacie…
PGMem: Tightly Coupled Persona-Memory Graph for Lifelong Personalized Agents
Wonjun Choi, Yerim Kim, Yukyung Lee +1
Long-term personalized dialogue agents must track user preferences as their personas evolve. Existing memory systems organize past events well, but store personas as flat profiles…
AI Writers Have a Consistent Stylometric Footprint, but AI Editors Do Not
Zhengyang Shan, Yukyung Lee, Sophie Hao
Text generated by large language models (LLMs) has been shown to be stylometrically distinct from human-written text (Andre et al., 2023; Shah et al., 2023; Opara, 2024; Soto et al…
Cliff Tokens: Analyzing Failure Trigger Tokens in LLM Mathematical Reasoning
Jaeyong Ko, Pilsung Kang, Jinu Lee +1
Large language models reach high accuracy in mathematical reasoning, but individual traces on the same problem diverge; some arrive at the correct answer while others fail. Prior w…
CIRF: Tokenizing Chain-of-Thoughts into Reusable Functional Units for Efficient Latent Reasoning in Large Language Models
Yukyung Lee, Yumeng Shen, Jinhyeong Park +2
Implicit Chain-of-Thought (CoT) reduces the inference cost of large language models by internalizing the explicit rationales. However, existing approaches typically lack alignment…
Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams
Yukyung Lee, Yebin Lim, Woojun Jung +2
Evaluating language models in streaming environments is critical, yet underexplored. Existing benchmarks either focus on single complex events or provide curated inputs for each qu…
RExBench: Can coding agents autonomously implement AI research extensions?
Nicholas Edwards, Yukyung Lee, Yujun Audrey Mao +3
Agents based on Large Language Models (LLMs) have shown promise for performing sophisticated software engineering tasks autonomously. In addition, there has been progress towards d…
A Gradient Accumulation Method for Dense Retriever under Memory Constraint
Jaehee Kim, Yukyung Lee, Pilsung Kang
InfoNCE loss is commonly used to train dense retriever in information retrieval tasks. It is well known that a large batch is essential to stable and effective training with InfoNC…
Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models
Yukyung Lee, Soonwon Ka, Bokyung Son +2
Large Language Models (LLMs) have impacted the writing process, enhancing productivity by collaborating with humans in content creation platforms. However, generating high-quality,…
CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists
Yukyung Lee, Joonghoon Kim, Jaehee Kim +4
Existing LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models.…
RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information
Gunho No, Yukyung Lee, Hyeongwon Kang +1
As the IT industry advances, system log data becomes increasingly crucial. Many computer systems rely on log texts for management due to restricted access to source code. The need…
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
Yukyung Lee, Jaehee Kim, Doyoon Kim +3
As the e-commerce market continues to expand and online transactions proliferate, customer reviews have emerged as a critical element in shaping the purchasing decisions of prospec…
DSTEA: Improving Dialogue State Tracking via Entity Adaptive Pre-training
Yukyung Lee, Takyoung Kim, Hoonsang Yoon +3
Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past r…
Mismatch between Multi-turn Dialogue and its Evaluation Metric in Dialogue State Tracking
Takyoung Kim, Hoonsang Yoon, Yukyung Lee +2
Dialogue state tracking (DST) aims to extract essential information from multi-turn dialogue situations and take appropriate actions. A belief state, one of the core pieces of info…
LAnoBERT: System Log Anomaly Detection based on BERT Masked Language Model
Yukyung Lee, Jina Kim, Pilsung Kang
The system log generated in a computer system refers to large-scale data that are collected simultaneously and used as the basic data for determining errors, intrusion and abnormal…
Oh My Mistake!: Toward Realistic Dialogue State Tracking including Turnback Utterances
Takyoung Kim, Yukyung Lee, Hoonsang Yoon +3
The primary purpose of dialogue state tracking (DST), a critical component of an end-to-end conversational system, is to build a model that responds well to real-world situations.…
MultiOIE: Multilingual Open Information Extraction Based on Multi-Head Attention with BERT
Youngbin Ro, Yukyung Lee, Pilsung Kang
In this paper, we propose MultiOIE, which performs open information extraction (open IE) by combining BERT with multi-head attention. Our model is a sequence-labeling system wi…