Meng Fang

I am an Assistant Professor in AI at University of Liverpool. I'm also a visiting (assistant) professor at Eindhoven University of Technology (TU/e). I co-lead the UTS NLP Group. I had been a research scientist / intern at Tencent Robotics X / AI, CSIRO and Microsoft Research Asia before.

My research goal is to build trustworthy and intelligent agents capable of human-like language understanding, reasoning, and decision-making. My main areas include NLP and RL.

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Moe
Updates
  • 2 papers accepted to ICLR 2024 and one of them is spotlight. Congratulations to our students and collaborators.
  • We have 3 new papers at ACL 2023 and 3 papers at NeurIPS 2023. Congratulations to our students and collaborators.
  • We have 2 new papers at ICLR 2023 and EACL 2023. Congratulations to our students and collaborators.
  • Our work on Untrained GNNs received the Best Paper Award at Learning on Graphs Conference 2022 (LoG 2022).
  • Looking for motivated prospective students working with us. Please contact me to discuss potential topics and PhD opportunites.
Research
Projects

Text-based games
TL;DR: We consider language understanding and reasoning for agents in text-based games.
Keywords: responsible AI, knowledge graphs, attention, RL, hierarchical RL. [project page]

Conversational AI
TL;DR: We consider chatbots for dialogue generation and reasoning.
Keywords: Language generation, persona. [project page]

Question & Answering
TL;DR: We consider the reasoning process for question and answering problems.
Keywords: Retrieval-augmented generation, open domain, knowledge graphs, graph neural networks [project page]

Reinforcement learning
TL;DR: We propose new agents and environments for robotics and Game AI.
Keywords: sparse/delayed rewards, sample efficient, multi-goal RL, continual learning. [project page]

Publications

Selected: (Full publication list)

Where Would I Go Next? Large Language Models as Human Mobility Predictors
Xinglei Wang*, Meng Fang*, Zichao Zeng, Tao Cheng
Preprint Aug 2023 [code]

RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering
Zihan Zhang, Meng Fang, Ling Chen
In ACL 2024 (Findings) [code]

Human-Guided Moral Decision Making in Text-based Games
Zijing Shi, Meng Fang, Ling Chen, Yali Du, Jun Wang
In AAAI 2024: Safe, Robut, and Responsible AI (SRRAI) [code]

Large Language Models Are Neurosymbolic Reasoners
Meng Fang*, Shilong Deng*, Yudi Zhang*, Zijing Shi, Ling Chen, Mykola Pechenizkiy, Jun Wang
In AAAI 2024 [code]

CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models
Jiaxu Zhao*, Meng Fang*, Zijing Shi, Yitong Li, Ling Chen, Mykola Pechenizkiy
In ACL 2023 [code]

NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
Iftitahu Nimah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy
In ACL 2023 [code]

A Survey for Efficient Open Domain Question Answering
Qin Zhang, Shangsi Chen, Dongkuan Xu, Qingqing Cao, Xiaojun Chen, Trevor Cohn, Meng Fang
In ACL 2023 [resource]

Stay Moral and Explore: Learn to Behave Morally in Text-based Games
Zijing Shi*, Meng Fang*, Yunqiu Xu, Ling Chen, Yali Du
In ICLR 2023 [code]

Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach
Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, Mykola Pechenizkiy
In NeurIPS 2023 [code]

COOM: A Game Benchmark for Continual Reinforcement Learning
Tristan Tomilin, Meng Fang, Yudi Zhang, Mykola Pechenizkiy
In NeurIPS 2023 [code]

Perceiving the World: Question-guided Reinforcement Learning for Text-based Games
Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang
In ACL 2022 [code]

Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Dongwon Kelvin Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Gholamreza Haffari
In ACL 2022 [code]

A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation
Yu Cao, Wei Bi, Meng Fang, Shuming Shi, Dacheng Tao
In ACL 2022 [code]

Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL
Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, Chongjie Zhang
In ICLR 2022 [code]

TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack
Yu Cao, Dianqi Li, Meng Fang, Tianyi Zhou, Jun Gao, Yibing Zhan, Dacheng Tao
In EMNLP 2022 [code]

Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics
Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad
In NAACL 2022 [code]

Phrase-level Textual Adversarial Attack with Label Preservation
Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy
In NAACL 2022 (Findings)

Generalization in Text-based Games via Hierarchical Reinforcement Learning
Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Chengqi Zhang
In EMNLP 2021 (Findings) [code]

DAGN: Discourse-Aware Graph Network for Logical Reasoning
Yinya Huang, Meng Fang, Yu Cao, Liwei Wang, Xiaodan Liang
In NAACL 2021 [Leaderboard: the 1st until 17th Nov., 2020] [code]

Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games
Yunqiu Xu*, Meng Fang*, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang
In NeurIPS 2020 [code]

Curriculum-guided hindsight experience replay
Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang
In NeurIPS 2019 [code]

DHER: Hindsight experience replay for dynamic goals
Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang
In ICLR 2019 [project webpage] [code]

Bag: Bi-directional attention entity graph convolutional network for multi-hop reasoning question answering
Yu Cao, Meng Fang, Dacheng Tao
In NAACL 2019 [code]

Learning how to Active Learn: A Deep Reinforcement Learning Approach
Meng Fang, Yuan Li, Trevor Cohn
In EMNLP 2017 [code]

Model transfer for tagging low-resource languages using a bilingual dictionary
Meng Fang, Trevor Cohn
In ACL 2017 [code]

Networked bandits with disjoint linear payoffs
Meng Fang, Dacheng Tao
In KDD 2014


Awards

Acknowledgements
I would like to thank all my collaborators, interns and students.

(imitation is the sincerest form of flattery)