9 papers
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…
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…