3 citations · 8 across the 41 of their papers we have counts for
21 papers · 2 filters
Don't Adapt Small Language Models for Tools; Adapt Tool Schemas to the Models
Jonggeun Lee, Woojung Song, Jongwook Han +2
Small language models (SLMs) enable scalable tool-augmented multi-agent systems where multiple SLMs handle subtasks orchestrated by a powerful coordinator. However, they struggle w…
Quantifying Data Contamination in Psychometric Evaluations of LLMs
Jongwook Han, Woojung Song, Jonggeun Lee +1
Recent studies apply psychometric questionnaires to Large Language Models (LLMs) to assess high-level psychological constructs such as values, personality, moral foundations, and d…
ThinkBrake: Efficient Reasoning via Log-Probability Margin Guided Decoding
Sangjun Song, Minjae Oh, Seungkyu Lee +2
Large Reasoning Models (LRMs) allocate substantial inference-time compute to Chain-of-Thought (CoT) reasoning, improving performance on mathematics, scientific QA, and tool usage.…
SUIT: Knowledge Editing with Subspace-Aware Key-Value Mappings
Haewon Park, Sangwoo Kim, Yohan Jo
Knowledge editing aims to efficiently correct factual errors in language models. Widely used locate-then-edit methods update an MLP layer by adjusting its weights to change the map…
Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models
Jongwook Han, Jongwon Lim, Injin Kong +1
Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during training, and (2) prompted expression, el…
SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents
Gyuhyeon Seo, Jungwoo Yang, Junseong Pyo +3
We introduce , a high-fidelity smart home simulator and a benchmark of 600 episodes for LLM-based smart home agents. Existing smart home benchmarks treat the hom…
Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
Yoonah Park, Haesung Pyun, Yohan Jo
While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to their internal knowledge. In partic…
Non-Collaborative User Simulators for Tool Agents
Jeonghoon Shim, Woojung Song, Cheyon Jin +2
Tool agents interact with users through multi-turn dialogues to accomplish various tasks. Recent studies have adopted user simulation methods to develop these agents in multi-turn…
Future Policy Approximation for Offline Reinforcement Learning in LLM Reasoning
Minjae Oh, Yunho Choi, Dongmin Choi +1
Reinforcement learning (RL) has emerged as a key driver of post-training for complex reasoning in large language models (LLMs), yet online RL introduces substantial instability and…
Human Psychometric Questionnaires Mischaracterize LLM Behavior
Woojung Song, Dongmin Choi, Yoonah Park +3
We examine whether human psychometric questionnaires can serve as reliable tools for characterizing and predicting LLM behavior in everyday user interactions. We analyze eight open…
In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents
Seungkyu Lee, Nalim Kim, Yohan Jo
Tool agents--LLM-based systems that interact with external APIs--offer a way to execute real-world tasks. However, as tasks become increasingly complex, these agents struggle to id…
Sparse and Dense Retrievers Learn Better Together: Joint Sparse-Dense Optimization for Text-Image Retrieval
Jonghyun Song, Youngjune Lee, Gyu-Hwung Cho +3
Vision-Language Pretrained (VLP) models have achieved impressive performance on multimodal tasks, including text-image retrieval, based on dense representations. Meanwhile, Learned…
Psychometric Item Validation Using Virtual Respondents with Trait-Response Mediators
Sungjib Lim, Woojung Song, Eun-Ju Lee +1
As psychometric surveys are increasingly used to assess the traits of large language models (LLMs), the need for scalable survey item generation suited for LLMs has also grown. A c…
Value Portrait: Assessing Language Models' Values through Psychometrically and Ecologically Valid Items
Jongwook Han, Dongmin Choi, Woojung Song +2
The importance of benchmarks for assessing the values of language models has been pronounced due to the growing need of more authentic, human-aligned responses. However, existing b…
Improving Dialogue State Tracking through Combinatorial Search for In-Context Examples
Haesung Pyun, Yoonah Park, Yohan Jo
In dialogue state tracking (DST), in-context learning comprises a retriever that selects labeled dialogues as in-context examples and a DST model that uses these examples to infer…
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings
Junseo Kim, Jongwook Han, Dongmin Choi +3
Visual persuasion, which uses visual elements to influence cognition and behaviors, is crucial in fields such as advertising and political communication. With recent advancements i…
Context-Robust Knowledge Editing for Language Models
Haewon Park, Gyubin Choi, Minjun Kim +1
Knowledge editing (KE) methods offer an efficient way to modify knowledge in large language models. Current KE evaluations typically assess editing success by considering only the…
ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models
Jeonghoon Shim, Gyuhyeon Seo, Cheongsu Lim +1
Tool-Augmented Language Models (TALMs) leverage external APIs to answer user queries across various domains. However, existing benchmark datasets for TALM research often feature si…
KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy
Hyunjong Kim, Suyeon Lee, Yeongjae Cho +4
The increasing demand for mental health services has led to the rise of AI-driven mental health chatbots, though challenges related to privacy, data collection, and expertise persi…
Dialogue Systems for Emotional Support via Value Reinforcement
Juhee Kim, Chunghu Mok, Jisun Lee +2
Emotional support dialogue systems aim to reduce help-seekers' distress and help them overcome challenges. While human values$\unicode{x2013}$core beliefs that shape an individual'…
Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
Yooseop Lee, Suin Kim, Yohan Jo
In designing multiple-choice questions (MCQs) in education, creating plausible distractors is crucial for identifying students' misconceptions and gaps in knowledge and accurately…