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Found 10 papers, 2 papers with code
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence.
Killer Apps: Low-Speed, Large-Scale AI Weapons
The accelerating advancements in Artificial Intelligence (AI) and Machine Learning (ML), highlighted by the development of cutting-edge Generative Pre-trained Transformer (GPT) models by organizations such as OpenAI, Meta, and Anthropic, present new challenges and opportunities in warfare and security.
Trapping LLM Hallucinations Using Tagged Context Prompts
Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents.
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.
Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models
Text analysis of social media for sentiment, topic analysis, and other analysis depends initially on the selection of keywords and phrases that will be used to create the research corpora.
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.
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.
Neural Embedding Allocation: Distributed Representations of Topic Models
Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents.
Dense Distributions from Sparse Samples: Improved Gibbs Sampling Parameter Estimators for LDA
We introduce a novel approach for estimating Latent Dirichlet Allocation (LDA) parameters from collapsed Gibbs samples (CGS), by leveraging the full conditional distributions over the latent variable assignments to efficiently average over multiple samples, for little more computational cost than drawing a single additional collapsed Gibbs sample.
