4 citations · 6 across the 11 of their papers we have counts for
15 papers
Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
Shirley Anugrah Hayati, Patricia Anugrah Setiani, Ruizi Wang +4
When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions? For solving long-horizon tasks, humans place greater importance on ongoing…
Into the Unknown: Benchmarking AI agents on open scientific research problems
Seungone Kim, Jiayi Geng, Young-Jun Lee +25
Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction…
Meta: Recursive Self-Improvement through Emergent Depth
Zae Myung Kim, Young-Jun Lee, Seungyeon Jwa +1
Self-improving LLM agents refine answers, not the process that produces those answers. Systems that add a meta-level hold that level fixed, and those that edit themselves must leav…
Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks
Young-Jun Lee, Seungone Kim, Minki Kang +5
Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture? Large Language Models (LLMs) integrated into evolutionary search h…
RefineBench: Evaluating Refinement Capability of Language Models via Checklists
Young-Jun Lee, Seungone Kim, Byung-Kwan Lee +6
Can language models (LMs) self-refine their own responses? This question is increasingly relevant as a wide range of real-world user interactions involve refinement requests. Howev…
AssurAI: Experience with Constructing Korean Socio-cultural Datasets to Discover Potential Risks of Generative AI
Chae-Gyun Lim, Seung-Ho Han, EunYoung Byun +51
The rapid evolution of generative AI necessitates robust safety evaluations. However, current safety datasets are predominantly English-centric, failing to capture specific risks i…
MultiVerse: A Multi-Turn Conversation Benchmark for Evaluating Large Vision and Language Models
Young-Jun Lee, Byung-Kwan Lee, Jianshu Zhang +9
Vision-and-Language Models (VLMs) have shown impressive capabilities on single-turn benchmarks, yet real-world applications often demand more intricate multi-turn dialogues. Existi…
LANGALIGN: Enhancing Non-English Language Models via Cross-Lingual Embedding Alignment
Jong Myoung Kim, Young-Jun Lee, Ho-Jin Choi +1
While Large Language Models have gained attention, many service developers still rely on embedding-based models due to practical constraints. In such cases, the quality of fine-tun…
PAD: Towards Efficient Data Generation for Transfer Learning Using Phrase Alignment
Jong Myoung Kim, Young-Jun_Lee, Ho-Jin Choi +1
Transfer learning leverages the abundance of English data to address the scarcity of resources in modeling non-English languages, such as Korean. In this study, we explore the pote…
Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model
Young-Jun Lee, Dokyong Lee, Junyoung Youn +2
To increase social bonding with interlocutors, humans naturally acquire the ability to respond appropriately in a given situation by considering which conversational skill is most…
Intriguing Properties of Large Language and Vision Models
Young-Jun Lee, Byungsoo Ko, Han-Gyu Kim +2
Recently, large language and vision models (LLVMs) have received significant attention and development efforts due to their remarkable generalization performance across a wide rang…
Does Incomplete Syntax Influence Korean Language Model? Focusing on Word Order and Case Markers
Jong Myoung Kim, Young-Jun Lee, Yong-jin Han +2
Syntactic elements, such as word order and case markers, are fundamental in natural language processing. Recent studies show that syntactic information boosts language model perfor…
Stark: Social Long-Term Multi-Modal Conversation with Persona Commonsense Knowledge
Young-Jun Lee, Dokyong Lee, Junyoung Youn +4
Humans share a wide variety of images related to their personal experiences within conversations via instant messaging tools. However, existing works focus on (1) image-sharing beh…
Large Language Models can Share Images, Too!
Young-Jun Lee, Dokyong Lee, Joo Won Sung +2
This paper explores the image-sharing capability of Large Language Models (LLMs), such as GPT-4 and LLaMA 2, in a zero-shot setting. To facilitate a comprehensive evaluation of LLM…
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset
Young-Jun Lee, Byungsoo Ko, Han-Gyu Kim +2
As sharing images in an instant message is a crucial factor, there has been active research on learning an image-text multi-modal dialogue models. However, training a well-generali…