Portrait of Bang Liu

Bang Liu

Associate Academic Member
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Data Mining
Deep Learning
Generative Models
Learning on Graphs
Natural Language Processing

Biography

Bang Liu is an assistant professor in the Department of Computer Science and Operations Research (DIRO), and a core member of the Applied Research in Computational Linguistics Lab (RALI) at Université de Montréal. He is also an associate academic member of Mila – Quebec Artificial Intelligence Institute and a Canada CIFAR AI Chair.

Liu received his BEng from the University of Science and Technology of China in 2013, and his MSc and PhD degrees from the University of Alberta in 2015 and 2020, respectively. His research interests lie primarily in the areas of natural language processing, multimodal and embodied learning, theory and techniques for AGI (e.g., understanding and improving large language models), and AI for science (e.g., health, material science, XR).

Current Students

Independent visiting researcher - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
Master's Research - Université de Montréal
Master's Research - Université de Montréal
Research Intern - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
Master's Research - Université de Montréal

Publications

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu
Keith G Mills
Jiao He
Di Niu
VCR: Visual Caption Restoration
Tianyu Zhang
Suyuchen Wang
Lu Li
Ge Zhang
Perouz Taslakian
Sai Rajeswar
Jie Fu
We introduce Visual Caption Restoration (VCR), a novel vision-language task that challenges models to accurately restore partially obscured … (see more)texts using pixel-level hints within images. This task stems from the observation that text embedded in images is intrinsically different from common visual elements and natural language due to the need to align the modalities of vision, text, and text embedded in images. While numerous works have integrated text embedded in images into visual question-answering tasks, approaches to these tasks generally rely on optical character recognition or masked language modeling, thus reducing the task to mainly text-based processing. However, text-based processing becomes ineffective in VCR as accurate text restoration depends on the combined information from provided images, context, and subtle cues from the tiny exposed areas of masked texts. We develop a pipeline to generate synthetic images for the VCR task using image-caption pairs, with adjustable caption visibility to control the task difficulty. With this pipeline, we construct a dataset for VCR called VCR-Wiki using images with captions from Wikipedia, comprising 2.11M English and 346K Chinese entities in both easy and hard split variants. Our results reveal that current vision language models significantly lag behind human performance in the VCR task, and merely fine-tuning the models on our dataset does not lead to notable improvements. We release VCR-Wiki and the data construction code to facilitate future research.
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Shengyao Lu
Keith G Mills
Jiao He
Di Niu
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for ex… (see more)plaining GNNs typically rely on training auxiliary models, resulting in the explanations remain black-boxed. This paper introduces Graph Output Attribution (GOAt), a novel method to attribute graph outputs to input graph features, creating GNN explanations that are faithful, discriminative, as well as stable across similar samples. By expanding the GNN as a sum of scalar products involving node features, edge features and activation patterns, we propose an efficient analytical method to compute contribution of each node or edge feature to each scalar product and aggregate the contributions from all scalar products in the expansion form to derive the importance of each node and edge. Through extensive experiments on synthetic and real-world data, we show that our method not only outperforms various state-of-the-art GNN explainers in terms of the commonly used fidelity metric, but also exhibits stronger discriminability, and stability by a remarkable margin.
Efficient Classification of Long Documents via State-Space Models
Peng Lu
Suyuchen Wang
Mehdi Rezagholizadeh
Ivan Kobyzev
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
Yu Song
Santiago Miret
Huan Zhang
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
Yuyan Chen
Zhihao Wen
Ge Fan
Zhengyu Chen
Wei Wu
Dayiheng Liu
Zhixu Li
Yanghua Xiao
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Xiaoqiang Wang
Siliang Tang
Lingfei Wu
Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models
Zhong Zhang
Junming Shao
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
Yurun Song
Santiago Miret
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang
Siliang Tang
Lingfei Wu
Existing metrics for assessing question generation not only require costly human reference but also fail to take into account the input cont… (see more)ext of generation, rendering the lack of deep understanding of the relevance between the generated questions and input contexts. As a result, they may wrongly penalize a legitimate and reasonable candidate question when it (1) involves complicated reasoning with the context or (2) can be grounded by multiple evidences in the context.In this paper, we propose QRelScore, a context-aware Relevance evaluation metric for Question Generation.Based on off-the-shelf language models such as BERT and GPT2, QRelScore employs both word-level hierarchical matching and sentence-level prompt-based generation to cope with the complicated reasoning and diverse generation from multiple evidences, respectively.Compared with existing metrics, our experiments demonstrate that QRelScore is able to achieve a higher correlation with human judgments while being much more robust to adversarial samples.
Better Modeling the Programming World with Code Concept Graphs-augmented Multi-modal Learning
Martin Weyssow
Houari Sahraoui
The progress made in code modeling has been tremendous in recent years thanks to the design of natural language processing learning approach… (see more)es based on state-of-the-art model architectures. Nevertheless, we believe that the current state-of-the-art does not focus enough on the full potential that data may bring to a learning process in software engineering. Our vision articulates on the idea of leveraging multi-modal learning approaches to modeling the programming world. In this paper, we investigate one of the underlying idea of our vision whose objective based on concept graphs of identifiers aims at leveraging high-level relationships between domain concepts manipulated through particular language constructs. In particular, we propose to enhance an existing pretrained language model of code by joint-learning it with a graph neural network based on our concept graphs. We conducted a preliminary evaluation that shows gain of effectiveness of the models for code search using a simple joint-learning method and prompts us to further investigate our research vision.
Grow-and-Clip: Informative-yet-Concise Evidence Distillation for Answer Explanation
Yuyan Chen
Yanghua Xiao
Interpreting the predictions of existing Question Answering (QA) models is critical to many real-world intelligent applications, such as QA … (see more)systems for healthcare, education, and finance. However, existing QA models lack interpretability and provide no feedback or explanation for end-users to help them understand why a specific prediction is the answer to a question. In this research, we argue that the evidences of an answer is critical to enhancing the interpretability of QA models. Unlike previous research that simply extracts several sentence(s) in the context as evidence, we are the first to explicitly define the concept of evidence as the supporting facts in a context which are informative, concise, and readable. Besides, we provide effective strategies to quantitatively measure the informativeness, conciseness and readability of evidence. Furthermore, we propose Grow-and-Clip Evidence Distillation (GCED) algorithm to extract evidences from the contexts by trade-off informativeness, conciseness, and readability. We conduct extensive experiments on the SQuAD and TriviaQA datasets with several baseline models to evaluate the effect of GCED on interpreting answers to questions. Human evaluation are also carried out to check the quality of distilled evidences. Experimental results show that automatic distilled evidences have human-like informativeness, conciseness and readability, which can enhance the interpretability of the answers to questions.