25 citations · 107 across the 30 of their papers we have counts for
9 papers · 1 filter
You've Changed: Detecting Modification of Black-Box Large Language Models
Alden Dima, James Foulds, Shimei Pan +1
Large Language Models (LLMs) are often provided as a service via an API, making it challenging for developers to detect changes in their behavior. We present an approach to monitor…
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Tao Zhang, Ziqian Zeng, Yuxiang Xiao +4
Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better…
RAGged Edges: The Double-Edged Sword of Retrieval-Augmented Chatbots
Philip Feldman, James R. Foulds, Shimei Pan
Large language models (LLMs) like ChatGPT demonstrate the remarkable progress of artificial intelligence. However, their tendency to hallucinate -- generate plausible but false inf…
Teach me with a Whisper: Enhancing Large Language Models for Analyzing Spoken Transcripts using Speech Embeddings
Fatema Hasan, Yulong Li, James Foulds +2
Speech data has rich acoustic and paralinguistic information with important cues for understanding a speaker's tone, emotion, and intent, yet traditional large language models such…
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…
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…
Analyzing COVID-19 Tweets with Transformer-based Language Models
Philip Feldman, Sim Tiwari, Charissa S. L. Cheah +2
This paper describes a method for using Transformer-based Language Models (TLMs) to understand public opinion from social media posts. In this approach, we train a set of GPT model…
Neural Embedding Allocation: Distributed Representations of Topic Models
Kamrun Naher Keya, Yannis Papanikolaou, James R. Foulds
Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents…
Mixed Membership Word Embeddings for Computational Social Science
James Foulds
Word embeddings improve the performance of NLP systems by revealing the hidden structural relationships between words. Despite their success in many applications, word embeddings h…