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14H034160212/README.md

Qiming Bao

Homepage   LinkedIn   GitHub   Gmail   Google Scholar   DBLP   Twitter   CV   简历

Qiming Bao is a Ph.D. Candidate at the Strong AI Lab, NAOInstitute, University of Auckland, New Zealand, supervised by Professor Michael Witbrock. His research interests include natural language processing and reasoning. He has over five years of research and development experience, and has published several papers in top conferences in the fields of AI/NLP/Reasoning, including ACL, IJCAI, ICLR, EACL, AAAI/EAAI, LLM@IJCAI, AGI@ICLR, and IJCLR-NeSy. His method named AMR-LDA (GPT-4 + AMR-LDA Prompt Augmentation) has achieved the #1 ranking on one of the most challenged logical reasoning reading comprehension leaderboards (ReClor) and we are the first group scored above 90% on the hidden test set around the world. Two of his logical reasoning datasets called PARARULE-Plus and AbductionRules have been collected by LogiTorch, ReasoningNLP, Prompt4ReasoningPapers and OpenAI/Evals. Qiming has given public guest talks and academic visit at Microsoft Research Asia, Samsung AI Center Cambridge UK, IEEE Vehicular Technology Society, ZJU-NLP Group, Zhejiang University, The University of Melbourne, Institute of Automation, Chinese Academy of Sciences, Shenzhen MSU-BIT University and University of Massachusetts - Amherst on his main research topic, "Natural Language Processing and Reasoning".

Qiming is an AI researcher and engineer at Xtracta in Auckland, New Zealand, where he used the PEFT adapter for continual training on the large multimodal models InternVL2 and Qwen2-VL for intelligent document processing. He investigated and implemented alternative attention mechanisms to extend the effective sequence length in multi-modal document processing models such as LayoutLMv3 and ERNIE-LayoutX. He replicated the multi-task, multimodal pre-training code for LayoutLMv3, which Microsoft did not open source, including masked language modelling, masked image modelling, and word-patch alignment. He integrated DeepSpeed and adapters into ERNIE-LayoutX and LayoutLMv3, which can reduce training costs, result in a smaller model size, and make it easier to deploy in the production environment. He successfully applied for the Research & Development Tax Incentive (RDTI) grants from Callaghan Innovation (New Zealand's Innovation Agency) for both 2022 and 2023, each offering a tax credit equal to 15% of eligible R&D expenditure. This credit can be utilised to reduce the income tax payable by the company. Prior to this role, he worked as a research and development engineer in AIIT at Peking University, where he focused on automatic abstract generation and GPT-2 based dialog chatbot development. Qiming also has a great deal of teaching experience, having worked as a teaching assistant for three years. He earned a Bachelor of Science (Honours) in Computer Science (First Class) from the University of Auckland and completed a Summer Research Internship with Scholarship in Precision Driven Health & Orion Health. In addition, he was selected as one of ten students to participate in the Summer Research Program funded by Precision Driven Health, where the main topic was developing a Medical Chatbot based on Deep Learning and Knowledge Graph.

Papers/Projects

  • [22 August 2024] Our paper (Qiming Bao, Gaël Gendron, Alex Peng, Wanjun Zhong, Neset Tan, Yang Chen, Michael Witbrock, Jiamou Liu) "Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoning" has been accepted by ICONIP-24 [Paper link] [Source code].

  • [16 May 2024] Our paper (Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gaël Gendron, Timothy Pistotti, Neşet Tan, Nathan Young, Yang Chen, Yonghua Zhu, Michael Witbrock and Jiamou Liu) "Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning" has been accepted for publication in the Findings of 62nd Annual Meeting of the Association for Computational Linguistics (ACL-24) [#1 on the ReClor Leaderboard] [Paper link] [Source code].

  • [17 April 2024] Our paper (Gaël Gendron, Qiming Bao, Michael Witbrock, Gillian Dobbie) "Large Language Models Are Not Strong Abstract Reasoners" has been accepted by IJCAI 2024 [Paper link] [Source code and evaluation platform].

  • [05 March 2024] Our paper (Qiming Bao, Juho Leinonen, Alex Peng, Wanjun Zhong, Timothy Pistotti, Alice Huang, Paul Denny, Michael Witbrock and Jiamou Liu) "Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models" has been accepted by AGI@ICLR 2024 [Paper link] [Source code].

  • [05 March 2024] Our paper (Gaël Gendron, Qiming Bao, Michael Witbrock, Gillian Dobbie) "Large Language Models Are Not Strong Abstract Reasoners Yet" has been accepted by AGI@ICLR 2024 [Paper link] [Source code and evaluation platform].

  • [01 February 2024] Our paper Our paper (Zhongsheng Wang, Jiamou Liu, Qiming Bao, Hongfei Rong, Jingfeng Zhang) "ChatLogic: Integrating Logic Programming with Large Language Models for Multi-step Reasoning" has been accepted by NucLeaR@AAAI 2024 [Paper link] [Source code].

  • [24 June 2023] Our paper (Qiming Bao, Gaël Gendron, Alex Peng, Wanjun Zhong, Neset Tan, Yang Chen, Michael Witbrock, Jiamou Liu) "A Systematic Evaluation of Large Language Models on Out-of-Distribution Logical Reasoning Tasks" has been accepted by LLM@IJCAI'23 [Paper link] [Source code].

  • [24 June 2023] Our paper (Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gaël Gendron, Timothy Pistotti, Neşet Tan, Nathan Young, Yang Chen, Yonghua Zhu, Michael Witbrock and Jiamou Liu) "Enhancing Logical Reasoning of Large Language Models through Logic-Driven Data Augmentation" has been accepted by LLM@IJCAI'23 [#1 on the ReClor Leaderboard] [Paper link] [Source code].

News

Qiming Bao's GitHub stats

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  1. HHH-An-Online-Question-Answering-System-for-Medical-Questions HHH-An-Online-Question-Answering-System-for-Medical-Questions Public

    HBAM: Hierarchical Bi-directional Word Attention Model

    Python 85 32

  2. Strong-AI-Lab/A-Neural-Symbolic-Paradigm Strong-AI-Lab/A-Neural-Symbolic-Paradigm Public

    From Symbolic Logic Reasoning to Soft Reasoning: A Neural-Symbolic Paradigm

    Python 10 1

  3. Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language Strong-AI-Lab/Multi-Step-Deductive-Reasoning-Over-Natural-Language Public

    Multi-Step Deductive Reasoning Over Natural Language: An Empirical Study on Out-of-Distribution Generalisation

    Python 7 3

  4. Strong-AI-Lab/PARARULE-Plus Strong-AI-Lab/PARARULE-Plus Public

    PARARULE Plus: A Larger Deep Multi-Step Reasoning Dataset over Natural Language

    Python 6

  5. Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning Public

    The source code for Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning. #1 on the ReClor Leaderboard and we are the first group scored above 90% on the hidde…

    Python 13 1

  6. Strong-AI-Lab/Logical-and-abstract-reasoning Strong-AI-Lab/Logical-and-abstract-reasoning Public

    Evaluation on Logical Reasoning and Abstract Reasoning Challenges

    Python 20 6