15 citations · 34 across the 16 of their papers we have counts for
17 papers
-Bench: Evaluating Speech Interaction Models as Scientific Voice Assistants
Heyang Liu, Jiayi Huang, Wenyang Xiao +8
The advance of multimodal large language models (MLLMs) has fundamentally reshaped the paradigm of human-computer interaction, especially speech interaction models capable of seaml…
LaSR: Context-Aware Speech Recognition via Latent Reasoning
Heyang Liu, Ziyang Cheng, Jiayi Huang +5
Speech recognition in specialized domains requires leveraging contextual or topical information to improve the recognition of domain-specific entities. Speech Large Language Models…
PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling
Zefan Cai, Yichi Zhang, Bofei Gao +8
In this study, we investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Ou…
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…
Personalized Abstractive Summarization by Tri-agent Generation Pipeline
Wen Xiao, Yujia Xie, Giuseppe Carenini +1
Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we pro…
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
Chuyuan Li, Patrick Huber, Wen Xiao +3
Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matric…
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
Wen Xiao, Lesly Miculicich, Yang Liu +2
Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and…
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Wen Xiao, Giuseppe Carenini
Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document. In th…
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…
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
Wen Xiao, Iz Beltagy, Giuseppe Carenini +1
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts…
T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
Raymond Li, Wen Xiao, Lanjun Wang +2
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual…
W-RST: Towards a Weighted RST-style Discourse Framework
Patrick Huber, Wen Xiao, Giuseppe Carenini
Aiming for a better integration of data-driven and linguistically-inspired approaches, we explore whether RST Nuclearity, assigning a binary assessment of importance between text s…
Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning
Linzi Xing, Wen Xiao, Giuseppe Carenini
In news articles the lead bias is a common phenomenon that usually dominates the learning signals for neural extractive summarizers, severely limiting their performance on data wit…
Predicting Discourse Trees from Transformer-based Neural Summarizers
Wen Xiao, Patrick Huber, Giuseppe Carenini
Previous work indicates that discourse information benefits summarization. In this paper, we explore whether this synergy between discourse and summarization is bidirectional, by i…
Do We Really Need That Many Parameters In Transformer For Extractive Summarization? Discourse Can Help !
Wen Xiao, Patrick Huber, Giuseppe Carenini
The multi-head self-attention of popular transformer models is widely used within Natural Language Processing (NLP), including for the task of extractive summarization. With the go…
Systematically Exploring Redundancy Reduction in Summarizing Long Documents
Wen Xiao, Giuseppe Carenini
Our analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents. Yet, redundancy reduction has not been thoroughly…
Extractive Summarization of Long Documents by Combining Global and Local Context
Wen Xiao, Giuseppe Carenini
In this paper, we propose a novel neural single document extractive summarization model for long documents, incorporating both the global context of the whole document and the loca…