7 citations · 17 across the 15 of their papers we have counts for
16 papers
Self-Assessment Reinforcement Learning for Search-State Awareness in Retrieval-Augmented Large Language Models
Taewhoo Lee, Minju Song, Jungwoo Park +6
Recent advances in search agents have enabled large language models to tackle complex tasks by retrieving and synthesizing external information. However, existing search agents oft…
Who Pays More for Safety? Measuring the Disparate Cost of Safety Alignment across Languages
Chanwoong Yoon, Jungsoo Park, Alan Ritter
Safety alignment helps models adhere to human values, but it often reduces response utility. We ask a critical but understudied question: Does safety alignment impose the cost equa…
AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale
Minbyul Jeong, Chanwoong Yoon
Agents learn to act through interaction with environments, yet the environments used for training are often manually constructed or synthesized around predefined tasks and benchmar…
Solar Open 2 Technical Report
Sungrae Park, Sanghoon Kim, Gyoungjin Gim +50
We present Solar Open 2, a 250B-A15B Mixture-of-Experts language model built for long-horizon agentic tasks, scaled up from Solar Open 1 (Solar Open 100B). To hold entire agent tra…
The Curious Case of Analogies: Investigating Analogical Reasoning in Large Language Models
Taewhoo Lee, Minju Song, Chanwoong Yoon +2
Analogical reasoning is at the core of human cognition, serving as an important foundation for a variety of intellectual activities. While prior work has shown that LLMs can repres…
Assessing LLM Reasoning Steps via Principal Knowledge Grounding
Hyeon Hwang, Yewon Cho, Chanwoong Yoon +5
Step-by-step reasoning has become a standard approach for large language models (LLMs) to tackle complex tasks. While this paradigm has proven effective, it raises a fundamental qu…
Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
Jungwoo Park, Taewhoo Lee, Chanwoong Yoon +2
Extreme activation outliers in Large Language Models (LLMs) critically degrade quantization performance, hindering efficient on-device deployment. While channel-wise operations and…
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information
Yein Park, Chanwoong Yoon, Jungwoo Park +2
While the ability of language models to elicit facts has been widely investigated, how they handle temporally changing facts remains underexplored. We discover Temporal Heads, spec…
Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
Jiwoong Sohn, Yein Park, Chanwoong Yoon +5
Large language models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge. While retrieval-augmented gene…
ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage
Taewhoo Lee, Chanwoong Yoon, Kyochul Jang +4
Recent advancements in large language models (LLM) capable of processing extremely long texts highlight the need for a dedicated evaluation benchmark to assess their long-context c…
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Yein Park, Chanwoong Yoon, Jungwoo Park +3
Large language models (LLMs) have brought significant changes to many aspects of our lives. However, assessing and ensuring their chronological knowledge remains challenging. Exist…
LAPIS: Language Model-Augmented Police Investigation System
Heedou Kim, Dain Kim, Jiwoo Lee +4
Crime situations are race against time. An AI-assisted criminal investigation system, providing prompt but precise legal counsel is in need for police officers. We introduce LAPIS…
CompAct: Compressing Retrieved Documents Actively for Question Answering
Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang +2
Retrieval-augmented generation supports language models to strengthen their factual groundings by providing external contexts. However, language models often face challenges when g…
OLAPH: Improving Factuality in Biomedical Long-form Question Answering
Minbyul Jeong, Hyeon Hwang, Chanwoong Yoon +2
In the medical domain, numerous scenarios necessitate the long-form generation ability of large language models (LLMs). Specifically, when addressing patients' questions, it is ess…
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks
Hyunjae Kim, Hyeon Hwang, Jiwoo Lee +7
While recent advancements in commercial large language models (LM) have shown promising results in medical tasks, their closed-source nature poses significant privacy and security…
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversation
Chanwoong Yoon, Gangwoo Kim, Byeongguk Jeon +3
Conversational search, unlike single-turn retrieval tasks, requires understanding the current question within a dialogue context. The common approach of rewrite-then-retrieve aims…