Search Results for author:
Found 83 papers, 58 papers with code
KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish
This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task.
Does Time Have Its Place? Temporal Heads: Where Language Models Recall Time-specific Information
While the ability of language models to elicit facts has been widely investigated, how they handle temporally changing facts remains underexplored.
GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization
One of the most effective ways to achieve explainability is incorporating the concept of gene regulatory networks (GRNs) in designing deep learning models such as VAEs.
KU AIGEN ICL EDI@BC8 Track 3: Advancing Phenotype Named Entity Recognition and Normalization for Dysmorphology Physical Examination Reports
The objective of BioCreative8 Track 3 is to extract phenotypic key medical findings embedded within EHR texts and subsequently normalize these findings to their Human Phenotype Ontology (HPO) terms.
Monet: Mixture of Monosemantic Experts for Transformers
Understanding the internal computations of large language models (LLMs) is crucial for aligning them with human values and preventing undesirable behaviors like toxic content generation.
Culinary Class Wars: Evaluating LLMs using ASH in Cuisine Transfer Task
We introduce the ASH (authenticity, sensitivity, harmony) benchmark to evaluate LLMs' recipe generation abilities in the cuisine transfer task, assessing their cultural accuracy and creativity in the culinary domain.
Rationale-Guided Retrieval Augmented Generation for Medical Question Answering
Large language models (LLM) hold significant potential for applications in biomedicine, but they struggle with hallucinations and outdated knowledge.
TurboHopp: Accelerated Molecule Scaffold Hopping with Consistency Models
Scaffold hopping is an efficient strategy that facilitates the identification of similar active compounds by strategically modifying the core structure of molecules, effectively narrowing the wide chemical space and enhancing the discovery of drug-like products.
ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage
To thoroughly examine the effectiveness of existing benchmarks, we introduce a new metric called information coverage (IC), which quantifies the proportion of the input context necessary for answering queries.
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains
Large language models (LLMs) have brought significant changes to many aspects of our lives.
CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement
Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in this endeavor.
Learning from Negative Samples in Generative Biomedical Entity Linking
However, these models are usually trained only with positive samples--entities that match the input mention's identifier--and do not explicitly learn from hard negative samples, which are entities that look similar but have different meanings.
LAPIS: Language Model-Augmented Police Investigation System
An AI-assisted criminal investigation system, providing prompt but precise legal counsel is in need for police officers.
DeepClair: Utilizing Market Forecasts for Effective Portfolio Selection
To integrate the forecasting model into a deep reinforcement learning-driven portfolio selection framework, we introduced a two-step strategy: first, pre-training the time-series model on market data, followed by fine-tuning the portfolio selection architecture using this model.
CompAct: Compressing Retrieved Documents Actively for Question Answering
Retrieval-augmented generation supports language models to strengthen their factual groundings by providing external contexts.
Augmenting Biomedical Named Entity Recognition with General-domain Resources
Specifically, our models consistently outperformed the baseline models in six out of eight entity types, achieving an average improvement of 0. 9% over the best baseline performance across eight entities.
KU-DMIS at EHRSQL 2024:Generating SQL query via question templatization in EHR
Transforming natural language questions into SQL queries is crucial for precise data retrieval from electronic health record (EHR) databases.
OLAPH: Improving Factuality in Biomedical Long-form Question Answering
We also propose OLAPH, a simple and novel framework that utilizes cost-effective and multifaceted automatic evaluation to construct a synthetic preference set and answers questions in our preferred manner.
CookingSense: A Culinary Knowledgebase with Multidisciplinary Assertions
This paper introduces CookingSense, a descriptive collection of knowledge assertions in the culinary domain extracted from various sources, including web data, scientific papers, and recipes, from which knowledge covering a broad range of aspects is acquired.
Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks
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 concerns, hindering their widespread use in the medical field.
Ranked #6 on
Zero-Shot Learning
on MedConceptsQA
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing
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 is required to effectively process natural language input to produce an accurate visual output.
Ask Optimal Questions: Aligning Large Language Models with Retriever's Preference in Conversational Search
Furthermore, we fine-tune a smaller LM using this dataset to align it with the retrievers' preferences as feedback.
Subgraph-level Universal Prompt Tuning
In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent.
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker Joints
Molecular core structures and R-groups are essential concepts in drug development.
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation.
UnionDet: Union-Level Detector Towards Real-Time Human-Object Interaction Detection
This is a major bottleneck in HOI detection inference time.
Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models
To cope with the challenge, we propose a novel framework, Tree of Clarifications (ToC): It recursively constructs a tree of disambiguations for the AQ -- via few-shot prompting leveraging external knowledge -- and uses it to generate a long-form answer.
Co-attention Graph Pooling for Efficient Pairwise Graph Interaction Learning
Graph Neural Networks (GNNs) have proven to be effective in processing and learning from graph-structured data.
Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank Regression
The proposed method is a semiparametric approach to AFT modeling that does not impose any distributional assumptions on the survival time distribution.
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain.
KitchenScale: Learning to predict ingredient quantities from recipe contexts
We introduce KitchenScale, a fine-tuned Pre-trained Language Model (PLM) that predicts a target ingredient's quantity and measurement unit given its recipe context.
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity
Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents widespread clinical application.
LIQUID: A Framework for List Question Answering Dataset Generation
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 annotations.
Ranked #1 on
Question Answering
on MultiSpanQA
Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora.
Enhancing Label Consistency on Document-level Named Entity Recognition
A notable advantage of NER is its consistency in extracting biomedical entities in a document context.
Ranked #1 on
Named Entity Recognition (NER)
on Gellus
RecipeMind: Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer
To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients.
Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations
Most weakly supervised named entity recognition (NER) models rely on domain-specific dictionaries provided by experts.
Deformable Graph Transformer
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision.
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data.
Optimizing Test-Time Query Representations for Dense Retrieval
In this paper, we introduce TOUR (Test-Time Optimization of Query Representations), which further optimizes instance-level query representations guided by signals from test-time retrieval results.
Generating Information-Seeking Conversations from Unlabeled Documents
In this paper, we introduce a novel framework, SIMSEEK, (Simulating information-Seeking conversation from unlabeled documents), and compare its two variants.
Lack of Fluency is Hurting Your Translation Model
While the most train sentences are created via automatic techniques such as crawling and sentence-alignment methods, the test sentences are annotated with the consideration of fluency by human.
BERN2: an advanced neural biomedical named entity recognition and normalization tool
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e. g. diseases and drugs) from the ever-growing biomedical literature.
Ranked #3 on
Named Entity Recognition (NER)
on BC4CHEMD
Simple Questions Generate Named Entity Recognition Datasets
Recent named entity recognition (NER) models often rely on human-annotated datasets, requiring the significant engagement of professional knowledge on the target domain and entities.
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge.
Towards simple time-to-event modeling: optimizing neural networks via rank regression
Time-to-event analysis, also known as survival analysis, aims to predict the first occurred event time, conditional on a set of features.
Can Language Models be Biomedical Knowledge Bases?
To this end, we create the BioLAMA benchmark, which is comprised of 49K biomedical factual knowledge triples for probing biomedical LMs.
FaVIQ: FAct Verification from Information-seeking Questions
Claims in FAVIQ are verified to be natural, contain little lexical bias, and require a complete understanding of the evidence for verification.
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering
One of the main challenges in conversational question answering (CQA) is to resolve the conversational dependency, such as anaphora and ellipsis.
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.
HOTR: End-to-End Human-Object Interaction Detection with Transformers
Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i. e., humans) and target (i. e., objects) of interaction, and ii) the classification of the interaction labels.
Ranked #16 on
Human-Object Interaction Detection
on V-COCO
Sequence tagging for biomedical extractive question answering
Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps.
Consistency Training with Virtual Adversarial Discrete Perturbation
Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.
Regularization for Long Named Entity Recognition
Pre-trained language models (PLMs) are used to solve NER tasks and tend to be biased toward dataset patterns such as length statistics, surface form, and skewed class distribution.
Ranked #5 on
Named Entity Recognition (NER)
on WNUT 2017
``Killing Me'' Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.
"Killing Me" Is Not a Spoiler: Spoiler Detection Model using Graph Neural Networks with Dependency Relation-Aware Attention Mechanism
Several machine learning-based spoiler detection models have been proposed recently to protect users from spoilers on review websites.
Learning to Balance with Incremental Learning
Classification tasks require balanced distribution of data in order to ensure the learner to be trained to generalize over all classes.
How Do Your Biomedical Named Entity Recognition Models Generalize to Novel Entities?
The number of biomedical literature on new biomedical concepts is rapidly increasing, which necessitates a reliable biomedical named entity recognition (BioNER) model for identifying new and unseen entity mentions.
Learning Dense Representations of Phrases at Scale
Open-domain question answering can be reformulated as a phrase retrieval problem, without the need for processing documents on-demand during inference (Seo et al., 2019).
Ranked #1 on
Question Answering
on Natural Questions (long)
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods
Motivated by the SSP property and a generalized Runge-Kutta method, we propose Strong Stability Preserving networks (SSP networks) which improve robustness against adversarial attacks.
MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System
Generating an investment strategy using advanced deep learning methods in stock markets has recently been a topic of interest.
Transferability of Natural Language Inference to Biomedical Question Answering
We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5. 59%), Factoid (+0. 53%), List type (+13. 58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B).
Answering Questions on COVID-19 in Real-Time
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it.
Biomedical Entity Representations with Synonym Marginalization
In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates.
Look at the First Sentence: Position Bias in Question Answering
In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e. g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions can learn spurious positional cues and fail to give answers in different positions.
Adversarial Subword Regularization for Robust Neural Machine Translation
Exposing diverse subword segmentations to neural machine translation (NMT) models often improves the robustness of machine translation as NMT models can experience various subword candidates.
Exploring The Spatial Reasoning Ability of Neural Models in Human IQ Tests
Also, we analyze how neural models solve spatial reasoning tests with visual aids.
Learning by Semantic Similarity Makes Abstractive Summarization Better
By harnessing pre-trained language models, summarization models had rapid progress recently.
Contextualized Sparse Representations for Real-Time Open-Domain Question Answering
Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.
Graph Transformer Networks
In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.
Pre-trained Language Model for Biomedical Question Answering
The recent success of question answering systems is largely attributed to pre-trained language models.
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.
A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining
Also, the traditional text mining tools do not consider overlapping entities, which are frequently observed in multi-type named entity recognition results.
Ranked #4 on
Named Entity Recognition (NER)
on LINNAEUS
SAIN: Self-Attentive Integration Network for Recommendation
To deal with this problem, content based recommendation models which use the auxiliary attributes of users and items have been proposed.
KitcheNette: Predicting and Recommending Food Ingredient Pairings using Siamese Neural Networks
As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers.
Self-Attention Graph Pooling
In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs.
Ranked #5 on
Graph Classification
on FRANKENSTEIN
Predicting Multiple Demographic Attributes with Task Specific Embedding Transformation and Attention Network
Second, they assumed that all transactions are equally important in predicting demographic attributes.
Global Stock Market Prediction Based on Stock Chart Images Using Deep Q-Network
Moreover, the results show that future stock prices can be predicted even if the training and testing procedures are done in different countries.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
Ranked #1 on
Named Entity Recognition (NER)
on Species-800
Typeface Completion with Generative Adversarial Networks
However, in designing a typeface, it is difficult to keep the style of various characters consistent, especially for languages with lots of morphological variations such as Chinese.
Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering
Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source.
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types.
Ranked #15 on
Named Entity Recognition (NER)
on BC5CDR
Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior.
