Publications (8)
Improving Topic Modeling by Distilling Soft Labels from Language Models
Raymond Li, Amirhossein Abaskohi, Chuyuan Li +2
Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with…
Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation
Raymond Li, Md Tawkat Islam Khondaker, Amirhossein Abaskohi +3
Retrieval-augmented generation (RAG) increasingly requires models to answer questions from multiple retrieved documents, where only some sources are relevant and the retrieved bund…
Visual Analytics for Generative Transformer Models
Raymond Li, Ruixin Yang, Wen Xiao +3
While transformer-based models have achieved state-of-the-art results in a variety of classification and generation tasks, their black-box nature makes them challenging for interpr…
Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models
Raymond Li, Gabriel Murray, Giuseppe Carenini
In this work, we propose a method that combines two popular research areas by injecting linguistic structures into pre-trained language models in the parameter-efficient fine-tunin…
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
Raymond Li, Wen Xiao, Linzi Xing +3
The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why an…
Analyzing Verbal and Nonverbal Features for Predicting Group Performance
Uliyana Kubasova, Gabriel Murray, McKenzie Braley
This work analyzes the efficacy of verbal and nonverbal features of group conversation for the task of automatic prediction of group task performance. We describe a new publicly av…
Stoic Ethics for Artificial Agents
Gabriel Murray
We present a position paper advocating the notion that Stoic philosophy and ethics can inform the development of ethical A.I. systems. This is in sharp contrast to most work on bui…
Diversity-Aware Coherence Loss for Improving Neural Topic Models
Raymond Li, Felipe González-Pizarro, Linzi Xing +2
The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, i…