Search Results for author:
Found 31 papers, 3 papers with code
Incorporating LIWC in Neural Networks to Improve Human Trait and Behavior Analysis in Low Resource Scenarios
In this research, we investigate diverse methods to incorporate Linguistic Inquiry and Word Count (LIWC), a widely-used psycholinguistic lexicon, in NN models to improve human trait and behavior analysis in low resource scenarios.
Incorporating medical knowledge in BERT for clinical relation extraction
In recent years pre-trained language models (PLM) such as BERT have proven to be very effective in diverse NLP tasks such as Information Extraction, Sentiment Analysis and Question Answering.
You've Changed: Detecting Modification of Black-Box Large Language Models
We present an approach to monitor LLMs for changes by comparing the distributions of linguistic and psycholinguistic features of generated text.
LLM-based Corroborating and Refuting Evidence Retrieval for Scientific Claim Verification
In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corroborating and refuting documents as evidence for scientific claim verification.
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Furthermore, we categorized the gender biases in the "rejected" responses of GenderAlign into 4 principal categories.
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence.
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings
This leads to an improved language model for analyzing spoken transcripts while avoiding an audio processing overhead at test time.
Trapping LLM Hallucinations Using Tagged Context Prompts
Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents.
The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference
We motivate the use of visualization in relation to target users and common NLP pipelines.
Fair Inference for Discrete Latent Variable Models
It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations.
Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction
In this paper we survey literature in the area of trust between a single human supervisor and a single agent subordinate to determine the nature and extent of this additional information and to characterize it into a taxonomy that can be leveraged by future researchers and intelligent agent practitioners.
User Acceptance of Gender Stereotypes in Automated Career Recommendations
In other words, our results demonstrate we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans.
Learning User Embeddings from Temporal Social Media Data: A Survey
User-generated data on social media contain rich information about who we are, what we like and how we make decisions.
Analyzing COVID-19 Tweets with Transformer-based Language Models
This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts.
Fair Representation Learning for Heterogeneous Information Networks
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness.
Equitable Allocation of Healthcare Resources with Fair Cox Models
Healthcare programs such as Medicaid provide crucial services to vulnerable populations, but due to limited resources, many of the individuals who need these services the most languish on waiting lists.
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification
Text features that are correlated with class labels, but do not directly cause them, are sometimesuseful for prediction, but they may not be insightful.
Neural Fair Collaborative Filtering
A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms.
Supervising Unsupervised Open Information Extraction Models
We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance.
Predicting Malware Attributes from Cybersecurity Texts
Text analytics is a useful tool for studying malware behavior and tracking emerging threats.
Bayesian Modeling of Intersectional Fairness: The Variance of Bias
Intersectionality is a framework that analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including race, gender, sexual orientation, class, and disability.
An Intersectional Definition of Fairness
We propose definitions of fairness in machine learning and artificial intelligence systems that are informed by the framework of intersectionality, a critical lens arising from the Humanities literature which analyzes how interlocking systems of power and oppression affect individuals along overlapping dimensions including gender, race, sexual orientation, class, and disability.
UMBC at SemEval-2018 Task 8: Understanding Text about Malware
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).
Automatically Infer Human Traits and Behavior from Social Media Data
Given the complexity of human minds and their behavioral flexibility, it requires sophisticated data analysis to sift through a large amount of human behavioral evidence to model human minds and to predict human behavior.
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts
In this pa-per, we describe a novel method to train domain-specificword embeddings from sparse texts.
Multi-View Unsupervised User Feature Embedding for Social Media-based Substance Use Prediction
In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems.
Social Media-based Substance Use Prediction
In this paper, we demonstrate how the state-of-the-art machine learning and text mining techniques can be used to build effective social media-based substance use detection systems.
\$1 Today or \$2 Tomorrow? The Answer is in Your Facebook Likes
In economics and psychology, delay discounting is often used to characterize how individuals choose between a smaller immediate reward and a larger delayed reward.
Personalized Emphasis Framing for Persuasive Message Generation
In this paper, we present a study on personalized emphasis framing which can be used to tailor the content of a message to enhance its appeal to different individuals.
