29 citations · 79 across the 23 of their papers we have counts for
24 papers
MedSNIP: Building and Benchmarking Snippet-Level Granularity for Medical Fact Verification
Hasan Iqbal, Sarfraz Ahmad, Hyunjae Kim +5
A medical claim's correctness often depends not on the claim alone, but on the clinical structure around it. A claim may require a lab reference range, a causal or conditional link…
Benchmarking Direct Preference Optimization for Medical Large Vision-Language Models
Dain Kim, Jiwoo Lee, Jaehoon Yun +4
Large Vision-Language Models (LVLMs) hold significant promise for medical applications, yet their deployment is often constrained by insufficient alignment and reliability. While D…
Memorization in Large Language Models in Medicine: Prevalence, Characteristics, and Implications
Anran Li, Lingfei Qian, Mengmeng Du +18
Large Language Models (LLMs) have demonstrated significant potential in medicine, with many studies adapting them through continued pre-training or fine-tuning on medical data to e…
Med-PRM: Medical Reasoning Models with Stepwise, Guideline-verified Process Rewards
Jaehoon Yun, Jiwoong Sohn, Jungwoo Park +9
Large language models have shown promise in clinical decision making, but current approaches struggle to localize and correct errors at specific steps of the reasoning process. Thi…
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models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge.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…
LEME: Open Large Language Models for Ophthalmology with Advanced Reasoning and Clinical Validation
Hyunjae Kim, Xuguang Ai, Sahana Srinivasan +27
The rising prevalence of eye diseases poses a growing public health burden. Large language models (LLMs) offer a promising path to reduce documentation workload and support clinica…
Learning from Negative Samples in Biomedical Generative Entity Linking
Chanhwi Kim, Hyunjae Kim, Sihyeon Park +3
Generative models have become widely used in biomedical entity linking (BioEL) due to their excellent performance and efficient memory usage. However, these models are usually trai…
Augmenting Biomedical Named Entity Recognition with General-domain Resources
Yu Yin, Hyunjae Kim, Xiao Xiao +6
Training a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. While several studies have employed mult…
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…
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing
Hyunjae Kim, Seunghyun Yoon, Trung Bui +4
Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model…
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
Gangwoo Kim, Hajung Kim, Lei Ji +7
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets wit…
LIQUID: A Framework for List Question Answering Dataset Generation
Seongyun Lee, Hyunjae Kim, Jaewoo Kang
Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotatio…
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon +1
Most weakly supervised named entity recognition (NER) models rely on domain-specific dictionaries provided by experts. This approach is infeasible in many domains where dictionarie…
Simple Questions Generate Named Entity Recognition Datasets
Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon +2
Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entitie…
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
Hyunjae Kim, Mujeen Sung, Wonjin Yoon +2
This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge. The main feature of this challenge is that th…
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering
Gangwoo Kim, Hyunjae Kim, Jungsoo Park +1
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis. However, existing approaches do no…
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Buru Chang, Inggeol Lee, Hyunjae Kim +1
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites. Although dependency relations between context…
How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?
Hyunjae Kim, Jaewoo Kang
The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifyi…
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests
Hyunjae Kim, Yookyung Koh, Jinheon Baek +1
Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few stu…
Look at the First Sentence: Position Bias in Question Answering
Miyoung Ko, Jinhyuk Lee, Hyunjae Kim +2
Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity a…
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network
Raehyun Kim, Hyunjae Kim, Janghyuk Lee +1
Most companies utilize demographic information to develop their strategy in a market. However, such information is not available to most retail companies. Several studies have been…
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Jinhyuk Lee, Seongjun Yun, Hyunjae Kim +2
Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source. As open-domain QA requires retrieving…