My passion lies in the captivating world of natural language processing, where I love delving into its intricacies and uncovering its hidden potential. An explorer at heart, I am captivated by the thrill of investigating novel concepts, while tedious, repetitive engineering tasks hold little allure for me.

My research interest includes y on the domain of QA, encompassing complex reasoning and multi-modal question-answering systems. I have garnered extensive research and engineering internship experience at the Chinese Academy of Sciences.To date, I have authored three papers as the first author, which have been published at AI conferences including the ICLR, EMNLP, and ICASSP, with total google scholar .

πŸ”₯ News

  • 2024.01: Β πŸŽ‰πŸŽ‰ Neural Comprehension has accept in ICLR 2024 Poster!
  • 2023.08: Β πŸŽ‰πŸŽ‰ We’ve released LMTuner, a groundbreaking system where anyone can train large models in just 5 minutes!
  • 2023.04: Β πŸŽ‰πŸŽ‰ We’ve created Neural Comprehension - a breakthrough enabling LLMs to master symbolic operations!

πŸ“ Publications

ICLR 2024 Poster
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Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks

Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Kang Liu, Jun Zhao

Project

  • We have enabled language models to more fundamental comprehension of the concepts, to achieve completely absolute accuracy in symbolic reasoning without additional tools.

OpenReview

EMNLP 2023 Findings
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Large Language Models are Better Reasoners with Self-Verification

Yixuan Weng, Minjun Zhu, Fei Xia, Bin Li, Shizhu He, Shengping Liu, Bin Sun, Kang Liu, Jun Zhao

Project

  • We have demonstrated that language models have the capability for self-verification, and can further improve their own reasoning abilities.

Demo Video

TPAMI
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Visual Answer Localization with Cross-Modal Mutual Knowledge Transfer

Shutao Li, Bin Li, Bin Sun, Yixuan Weng

Project

  • We design the text span-based predictor, where the input text question, video subtitles, and visual prompt features are jointly learned with the pre-trained language model for enhancing the joint semantic representations.
ICASSP 2023 Oral
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Visual Answer Localization with Cross-Modal Mutual Knowledge Transfer

Yixuan Weng, Bin Li

Project

  • We introduce a cross-modal mutual knowledge transfer approach for localizing visual answers in images and videos.
COLING 2024 Poster
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Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database

Minjun Zhu, Yixuan Weng, Shizhu He, Kang Liu, Haifeng Liu, Yang Jun, Jun Zhao

Project

  • We propose to conduct Graph-Hop - a novel multi-chains and multi-hops retrieval and reasoning paradigm in complex question answering. We construct a new benchmark called ReasonGraphQA, which provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning.
ICASSP 2023 Poster
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Learning To Locate Visual Answer In Video Corpus Using Question

Bin Li, Yixuan Weng, Bin Sun, Shutao Li

Project

  • We propose a novel approach to locate visual answers in a video corpus using a question.
ICASSP 2023 Poster
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Learning to Build Reasoning Chains by Reliable Path Retrieval

Minjun Zhu, Yixuan Weng, Shizhu He, Cunguang Wang, Kang Liu, Li Cai, Jun Zhao

Project

  • We propose ReliAble Path-retrieval (RAP) for complex QA over knowledge graphs, which iteratively retrieves multi-hop reasoning chains. It models chains comprehensively and introduces losses from two views. Experiments show state-of-the-art performance on evidence retrieval and QA. Additional results demonstrate the importance of modeling sequence information for evidence chains.
EACL 2023 Poster
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Find Parent then Label Children: A Two-stage Taxonomy Completion Method with Pre-trained Language Model

Fei Xia, Yixuan Weng, Shizhu He, Kang Liu, Jun Zhao

Project

  • Proposed two-stage ATTEMPT method for taxonomy completion. Inserts new concepts by finding parent and labeling children. Combines local nodes with prompts for natural sentences. Utilizes pre-trained language models for hypernym/hyponym recognition. Outperforms existing methods on taxonomy completion and extension tasks.
EMNLP 2022 Demo
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MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

Fei Xia, Bin Li, Yixuan Weng, Shizhu He, Kang Liu, Bin Sun, Shutao Li, Jun Zhao

Project

  • We propose MedConQA, a medical conversational QA system using knowledge graphs, to address weak scalability, insufficient knowledge, and poor controllability in existing systems. It is a pipeline framework with open-sourced modular tools for flexibility. We construct a Chinese Medical Knowledge Graph and a Chinese Medical CQA dataset to enable knowledge-grounded dialogues. We also use SoTA techniques to keep responses controllable, as validated through professional evaluations. Code, data, and tools are open-sourced to advance research.

Demo Video

Arxiv
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ControlLM: Crafting Diverse Personalities for Language Models

Yixuan Weng, Shizhu He, Kang Liu, Shengping Liu, Jun Zhao

Project

  • We have enabled to control the personality traits and behaviors of language models in real-time at inference without costly training interventions.
Arxiv
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Large Language Models Need Holistically Thought in Medical Conversational QA

Yixuan Weng, Bin Li, Fei Xia, Minjun Zhu, Bin Sun, Shizhu He, Kang Liu, Jun Zhao

Project

  • We propose a holistic thinking approach for improving the performance of large language models in both Chinese and English medical conversational QA task.
Arxiv
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LMTuner: An user-friendly and highly-integrable Training Framework for fine-tuning Large Language Models

Yixuan Weng, Zhiqi Wang, Huanxuan Liao, Shizhu He, Shengping Liu, Kang Liu, Jun Zhao

Project

  • We advocate that LMTuner’s usability and integrality alleviate the complexities in training large language models. Remarkably, even a novice user could commence training large language models within five minutes. Furthermore, it integrates DeepSpeed frameworks and supports Efficient Fine-Tuning methodologies like Low Rank Adaptation (LoRA), Quantized LoRA (QLoRA), etc.,

Demo Video Homepage

πŸŽ– Honors and Awards

  • 2022.05 BioNLP-2022: Medical Video Classification, First Place
  • 2022.04 CBLUE First Place
  • 2022.01 SemEval22-Task3 PreTENS, First Place
  • 2021.11 SDU@AAAI-22: Acronym Disambiguation, First Place

πŸ’» Internships

  • 2021.09 - , CASIA, China.