19 citations · 37 across the 23 of their papers we have counts for
11 papers · 1 filter
Monet: Mixture of Monosemantic Experts for Transformers
Jungwoo Park, Young Jin Ahn, Kee-Eung Kim +1
Understanding the internal computations of large language models (LLMs) is crucial for aligning them with human values and preventing undesirable behaviors like toxic content gener…
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models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge.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…
PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science
Jian Xu, Chao Yu, Jiawei Xu +6
Papers, patents, and clinical trials are essential scientific resources in biomedicine, crucial for knowledge sharing and dissemination. However, these documents are often stored i…
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…
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
Donghee Choi, Mogan Gim, Donghyeon Park +4
This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, a…
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…
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
Minbyul Jeong, Jiwoong Sohn, Mujeen Sung +1
Recent proprietary large language models (LLMs), such as GPT-4, have achieved a milestone in tackling diverse challenges in the biomedical domain, ranging from multiple-choice ques…