Xinyuan Wang

Hi! I am Xinyuan Wang (王心远).

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I am an incoming Ph.D. student at HKU, mentored by Prof. Tao Yu. I obtained my master’s degree from University of California, San Diego (UCSD). I was luckily to be mentored two distinguished professors at UCSD in Natural Language Processing and Computer Vision - Prof. Zhiting Hu and Prof. Zhuowen Tu. Prior to my study at UCSD, I graduated from Central South University (CSU) in Hunan, China, where I was mentored by Prof. Ying Zhao.

Research Interests

  • Artificial Intelligence Agents: Designing and developing LLM/VLM based agent system that can execute in real-world environments.
  • Large Language Models (LLMs) with World Models: Augmenting LLMs with a world model formulation to enable principled decision-making, planning, and simulation. Enhancing the LLM’s abilities in reasoning, planning, and interacting with the world. (LLM Reasoners)
  • Foundation Model Prompting: Employing interpretable prompting to bridge the domain gap between user objectives and the outputs of foundation models. Effectively boosting the performance of foundation models on complex tasks through efficient and effective prompting. (PromptAgent)

Research Overview

My research interests are LLM Augmentation (Prompting, Reasoning), LLM Agent, Unsupervised Learning (Generative Models), and Multi-modal Models. In Prof. Zhiting Hu’s group, I worked on automatic LLM prompt optimization with Zhen. Recently our paper PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization is accepted by ICLR 2024. I am also working on LLM Reasoning by contributing to the LLM Reasoners library, which ensembles the most recent LLM reasoning methods and models. In Prof. Zhuowen Tu’s group, we are working on how to inprove diffusion models’ conceptual performance with an end-to-end loss. During my undergraduate years, I was mentored by Prof. Ying Zhao and worked on Interpretation of Convolutional Neural Networks and Visualization. Here is my graduate thesis: The Research on The Interpretability Method of DeepNeural Network Based on Average Image

How to contact me

Email: xiw136@ucsd.edu (till Jun. 2024) / xywang626@gmail.com

News

Apr 8, 2024 LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models is accepted by ICLR2024 workshop!
Jan 16, 2024 PromptAgent is accepted by ICLR 2024 (The Twelfth International Conference on Learning Representations)!
Nov 17, 2023 PromptAgent’s poster is presented at SoCal NLP 2023 at UCLA, Los Angeles, CA!
Oct 25, 2023 Paper published on Arxiv! PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Sep 1, 2022 Start my Master of Science Computer Science program at UC San Diego!

Selected Publications

  1. llm_reasoners_preview.png
    LLM Reasoners: New Evaluation, Library, and Analysis of Step-by-Step Reasoning with Large Language Models
    Shibo Hao, Yi Gu, Haotian Luo, and 8 more authors
    arXiv preprint arXiv:2404.05221, 2024
  2. promptagent_header.png
    PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
    Xinyuan Wang, Chenxi Li, Zhen Wang, and 6 more authors
    [ICLR 2024] The Twelfth International Conference on Learning Representations, 2024
  3. medicalbert.png
    Reduce the medical burden: An automatic medical triage system using text classification BERT based on Transformer structure
    Xinyuan Wang, Make Tao, Runpu Wang, and 1 more author
    In 2021 2nd International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), 2021