25 citations · 39 across the 16 of their papers we have counts for
6 papers · 1 filter
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…