32 citations · 95 across the 20 of their papers we have counts for
24 papers
Trapping LLM Hallucinations Using Tagged Context Prompts
Philip Feldman, James R. Foulds, Shimei Pan
Recent advances in large language models (LLMs), such as ChatGPT, have led to highly sophisticated conversation agents. However, these models suffer from "hallucinations," where th…
The Keyword Explorer Suite: A Toolkit for Understanding Online Populations
Philip Feldman, Shimei Pan, James R. Foulds
We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pip…
The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference
Richard Brath, Daniel Keim, Johannes Knittel +3
With a constant increase of learned parameters, modern neural language models become increasingly more powerful. Yet, explaining these complex model's behavior remains a widely uns…
Fair Inference for Discrete Latent Variable Models
Rashidul Islam, Shimei Pan, James R. Foulds
It is now well understood that machine learning models, trained on data without due care, often exhibit unfair and discriminatory behavior against certain populations. Traditional…
Tell Me Something That Will Help Me Trust You: A Survey of Trust Calibration in Human-Agent Interaction
George J. Cancro, Shimei Pan, James Foulds
When a human receives a prediction or recommended course of action from an intelligent agent, what additional information, beyond the prediction or recommendation itself, does the…
Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models
Philip Feldman, Aaron Dant, James R. Foulds +1
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…