Portrait de Bang Liu

Bang Liu

Membre académique associé
bang.liu@mila.quebec
Chaire en IA Canada-CIFAR
Professeur agrégé, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Apprentissage profond
Apprentissage sur graphes
Exploration des données
Modèles génératifs
Traitement du langage naturel
Site web
Google Scholar

Biographie

Bang Liu est professeur adjoint au Département d'informatique et de recherche opérationnelle (DIRO) de l'Université de Montréal. Il est membre du Laboratoire de recherche appliquée en linguistique informatique (RALI) du DIRO, membre associé de Mila – Institut québécois d'intelligence artificielle, et titulaire d'une chaire en IA Canada-CIFAR.

Il a obtenu un baccalauréat en ingénierie de l'Université des sciences et technologies de Chine (USTC) en 2013, ainsi qu’une maîtrise ès sciences et un doctorat de l'Université de l'Alberta en 2015 et en 2020, respectivement. Ses recherches portent principalement sur le traitement du langage naturel, l'apprentissage multimodal et incarné, la théorie et les techniques de l'intelligence artificielle (par exemple, la compréhension et l'amélioration de grands modèles de langage) et l'intelligence artificielle pour la science (par exemple, la santé, la science des matériaux et la radiologie).

Étudiants actuels

Publications

EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu
Bang Liu
Keith G Mills
Jiao He
Di Niu
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
proceedings.mlr.press
proceedings.mlr.press
Uncovering the essence of diverse media biases from the semantic embedding space
Hong Huang
Hua Zhu
Wenshi Liu
Hua Gao
Hai Jin
Bang Liu
Media bias widely exists in the articles published by news media, influencing their readers’ perceptions, and bringing prejudice or injust… (voir plus)ice to society. However, current analysis methods usually rely on human efforts or only focus on a specific type of bias, which cannot capture the varying magnitudes, connections, and dynamics of multiple biases, thus remaining insufficient to provide a deep insight into media bias. Inspired by the Cognitive Miser and Semantic Differential theories in psychology, and leveraging embedding techniques in the field of natural language processing, this study proposes a general media bias analysis framework that can uncover biased information in the semantic embedding space on a large scale and objectively quantify it on diverse topics. More than 8 million event records and 1.2 million news articles are collected to conduct this study. The findings indicate that media bias is highly regional and sensitive to popular events at the time, such as the Russia-Ukraine conflict. Furthermore, the results reveal some notable phenomena of media bias among multiple U.S. news outlets. While they exhibit diverse biases on different topics, some stereotypes are common, such as gender bias. This framework will be instrumental in helping people have a clearer insight into media bias and then fight against it to create a more fair and objective news environment.
2024-05-21
Humanities and Social Sciences Communications (publié)
doi.org
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Shengyao Lu
Keith G Mills
Jiao He
Bang Liu
Di Niu
Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability. Most existing methods for ex… (voir plus)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.
2024-01-15
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Efficient Classification of Long Documents via State-Space Models
Peng Lu
Suyuchen Wang
Mehdi Rezagholizadeh
Bang Liu
Ivan Kobyzev
2023-10-06
EMNLP/2023/Conference (accepté)
openreview.net
openreview.net
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
Yu Song
Santiago Miret
Huan Zhang
Bang Liu
2023-10-06
EMNLP/2023/Conference (accepté)
openreview.net
openreview.net
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
Bang Liu
Yanghua Xiao
2023-10-06
EMNLP/2023/Conference (accepté)
openreview.net
openreview.net
XMQAs: Constructing Complex-Modified Question-Answering Dataset for Robust Question Understanding
Yuyan Chen
Yanghua Xiao
Zhixu Li
Bang Liu
Question understanding is an important issue to the success of a Knowledge-based Question Answering (KBQA) system.However, the existing stud… (voir plus)y does not pay enough attention to this issue given that the questions in the existing KBQA datasets are usually expressed in simple and straightforward way. This is not in line with the actual linguistic conventions, which often use a lot of modifiers. To facilitate the study on evaluating and enhancing the question understanding ability of the KBQA systems, this paper proposes to construct a complex-modified question-answering (XMQAs) dataset based on existing KBQA datasets. With the help of knowledge bases and dictionaries, three kinds of modifiers are defined and applied to original simple-expressed questions. These modifiers could make the expression of these questions complex without changing their semantics. Based on XMQAs, we then propose a novel question understanding algorithm upon existing KBQA models, which greatly improves the robustness of their question understanding abilities. We conduct extensive experiments on XMQAs and two widely acknowledged KBQA datasets. The empirical results demonstrate that our proposed algorithm can improve the performance of KBQA models on not only the complex-modified questions, but also simple-expressed questions.
2023-08-09
IEEE Transactions on Knowledge and Data Engineering (inconnu)
doi.org
SkillQG: Learning to Generate Question for Reading Comprehension Assessment
Xiaoqiang Wang
Bang Liu
Siliang Tang
Lingfei Wu
2023-06-30
Findings of the Association for Computational Linguistics: ACL 2023 (publié)
doi.org
arxiv.org
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
Yurun Song
Santiago Miret
Bang Liu
2023-05-13
ArXiv (prépublication)
doi.org
arxiv.org
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
Xinlin Li
Bang Liu
Rui Heng Yang
Vanessa Courville
Chao Xing
Vahid Partovi Nia
Efficiently deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements. T… (voir plus)o address this issue, researchers have proposed multiplication-free neural networks, such as Power-of-Two quantization, or also known as Shift networks, which aim to reduce memory usage and simplify computation. However, existing low-bit Shift networks are not as accurate as their full-precision counterparts, typically suffering from limited weight range encoding schemes and quantization loss. In this paper, we propose the DenseShift network, which significantly improves the accuracy of Shift networks, achieving competitive performance to full-precision networks for vision and speech applications. In addition, we introduce a method to deploy an efficient DenseShift network using non-quantized floating-point activations, while obtaining 1.6X speed-up over existing methods. To achieve this, we demonstrate that zero-weight values in low-bit Shift networks do not contribute to model capacity and negatively impact inference computation. To address this issue, we propose a zero-free shifting mechanism that simplifies inference and increases model capacity. We further propose a sign-scale decomposition design to enhance training efficiency and a low-variance random initialization strategy to improve the model's transfer learning performance. Our extensive experiments on various computer vision and speech tasks demonstrate that DenseShift outperforms existing low-bit multiplication-free networks and achieves competitive performance compared to full-precision networks. Furthermore, our proposed approach exhibits strong transfer learning performance without a drop in accuracy. Our code was released on GitHub.
2022-12-31
IEEE/CVF Conference on Computer Vision and Pattern Recognition (publié)
doi.org
arxiv.org
Fine-tuning Happens in Tiny Subspaces: Exploring Intrinsic Task-specific Subspaces of Pre-trained Language Models
Zhong Zhang
Bang Liu
Junming Shao
2022-12-31
ACL (1) (publié)
doi.org
arxiv.org
QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance
Xiaoqiang Wang
Bang Liu
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… (voir plus)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.
2022-11-30
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org