Search Results for author:
Found 16 papers, 4 papers with code
ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Language
Handling implicit language is essential for natural language processing systems to achieve precise text understanding and facilitate natural interactions with users.
Long-Tailed Backdoor Attack Using Dynamic Data Augmentation Operations
Through simultaneous optimization of the backdoored model and trigger generator, guided by dynamic data augmentation operation selectors, we achieve significant advancements.
Backdooring Vision-Language Models with Out-Of-Distribution Data
The emergence of Vision-Language Models (VLMs) represents a significant advancement in integrating computer vision with Large Language Models (LLMs) to generate detailed text descriptions from visual inputs.
TrojVLM: Backdoor Attack Against Vision Language Models
The emergence of Vision Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to produce detailed text descriptions based on visual inputs, yet it introduces new security vulnerabilities.
Recent Trends of Multimodal Affective Computing: A Survey from NLP Perspective
The goal of this survey is to explore the current landscape of multimodal affective research, identify development trends, and highlight the similarities and differences across various tasks, offering a comprehensive report on the recent progress in multimodal affective computing from an NLP perspective.
Aspect-Based Sentiment Analysis
Emotion Recognition in Conversation
+3
BadCLM: Backdoor Attack in Clinical Language Models for Electronic Health Records
The advent of clinical language models integrated into electronic health records (EHR) for clinical decision support has marked a significant advancement, leveraging the depth of clinical notes for improved decision-making.
Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey
Large Language Models (LLMs) have achieved unparalleled success across diverse language modeling tasks in recent years.
Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compression
We also propose GSM8K-aug, i. e., an extended version of the existing GSM8K benchmark for task-agnostic prompts in order to provide a comprehensive evaluation platform.
Task-Agnostic Detector for Insertion-Based Backdoor Attacks
Textual backdoor attacks pose significant security threats.
Attention-Enhancing Backdoor Attacks Against BERT-based Models
Recent studies have revealed that \textit{Backdoor Attacks} can threaten the safety of natural language processing (NLP) models.
Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction
Class imbalance problems widely exist in the medical field and heavily deteriorates performance of clinical predictive models.
A Multimodal Transformer: Fusing Clinical Notes with Structured EHR Data for Interpretable In-Hospital Mortality Prediction
Deep-learning-based clinical decision support using structured electronic health records (EHR) has been an active research area for predicting risks of mortality and diseases.
A Study of the Attention Abnormality in Trojaned BERTs
Trojan attacks raise serious security concerns.
CUNY-PKU Parser at SemEval-2019 Task 1: Cross-Lingual Semantic Parsing with UCCA
This paper describes the systems of the CUNY-PKU team in SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA.
Transfer Learning for Scientific Data Chain Extraction in Small Chemical Corpus with BERT-CRF Model
Computational chemistry develops fast in recent years due to the rapid growth and breakthroughs in AI.
