Jiangjie Chen

Jiangjie Chen




Jiangjie Chen (陈江捷) is a researcher at ByteDance Seed Team. In 2024, he earned his Ph.D. at Fudan University in the School of Computer Science, Shanghai, China. His interested research topics are mostly around autonomous generative agents, including (but are not limited to):

  1. Reasoning and Planning: Advancing research on equipping generative agents with human-level reasoning and complex planning capabilities. This involves designing and implementing methodologies to incorporate decision-making, counterfactual thinking, and other complex reasoning tasks in generative models.
  2. Autonomous Generative Agents: Developing advanced methods for autonomous, trustworthy, and personalized language agents. This extends towards the exploration of their interactions with multiple agents and real environments.
  3. Cognitive Modeling of Language Models: Focusing on integrating elements from cognitive science into language models, such as the aspects of belief systems, analogical reasoning, Theory-of-Mind, etc. The goal is to augment the understanding of these agents regarding themselves and others, hence enabling them to generate more cognitively-aligned and human-like responses.
  • Large Language Models
  • Reasoning and Planning
  • Mountaineering 🧗‍♂️
  • Tennis 🎾
  • Musicals
  • Ph.D. in CS, 2019 - 2024

    Fudan University

  • B.S. in CS (honors), 2014 - 2019

    Fudan University


  • Jul. 2024: Our work on Irrelevant Evidence got accepted in COLM 2024!

  • Jul. 2024: I will join ByteDance Seed Team as a Full-time researcher.

  • Jun. 2024: How to automatically extend the specialized agent to multi-agent systems to improve task-solving capability? We propose EvoAgent, a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm. EvoAgent can be generalized to any LLM-based agent framework, and significantly enhance the task-solving capabilities of LLM-based agents!

  • Jun. 2024: Want your agents to win an auction for you? But does your agent know what it means by such a vague and high-level goal as “winning an auction”? Check out SelfGoal! We propose an automatic approach that enhances language agents’ capabilities to achieve high-level goals with limited instructions and delayed feedback by adaptively breaking down goals into practical subgoals. Really excited about automating agents to do high-level task with minimal human instructions!

  • Jun. 2024: TravelPlanner got a Spotlight recommendation at ICML 2024!

  • May 2024: Just defended my thesis, officially a Dr. :)

  • May 2024: Four papers are accepted to the main conference of ACL 2024! They are: TimeArena, AnalogyKB, InCharacter and GumbelSoft! See you in Bangkok :)

  • May 2024: Our TravelPlanner got accepted to ICML 2024!

  • Apr. 2024: The first Survey on Role-Playing Agents is out! Dive into our comprehensive survey of RPLA technologies, their applications, and the exciting potential for human-AI coexistence. Understanding role-playing paves the way for both personalized assistants and multi-agent society. Check our latest survey on role-playing agent!

  • Apr. 2024: Checkout SurveyAgent! This system stands out by offering a unified platform that supports researchers through various stages of their literature review process, facilitated by a conversational interface that prioritizes user interaction and personalization! Access via homepage and have fun!

  • Apr. 2024: In our new work, we extend our previous work on knowledge conflict during RAG, and find that LLMs are not robust to various types of irrelevant evidence in the context, which are very much lethal to RAG applications!

  • Mar. 2024: Agent Group Chat is out! In this paper, we build a multi-agent simulation that studies the impact of language on collective human behavior, using diverse scenarios such as inheritance disputes and philosophical debates. The simulation revealed that agents, when given complex language abilities and diverse personalities, can exhibit emergent behaviors that mirror human unpredictability. TLDR: Let’s scale up the number and diversity of agents and their interactions!

  • Mar. 2024: TravelPlanner and EasyTool are accepted to the LLM Agent Workshop @ICLR 2024 :)!

  • Feb. 2024: Check out TimeArena! In TimeArena, we built a simulated textual environment for language agents to complete multiple tasks in the shortest time, which is a very realistic setting of human world and also a great challenge for SoTA LLMs. Check out this project page for more details!

  • Feb. 2024: Check out GumbelSoft, a watermark for LLMs that allows for diversified text generation while remaining detectability!

  • Feb. 2024: Check out InCharacter! Self-assessments on RPAs are inherently flawed - which heavily depends on LLM’s own understanding of Personality. Instead, our work revolves around interviewing characters in 14 different psychological scales, providing a more objective description of LLM’s role play abilities. Check out this project demo!

  • Feb. 2024: TravelPlanner is out! We evaluate the planning ability of LLM agents using a real-world setup: Travel Planning, which is prominent for the challenge of integrating so many types of realistic constraints, like “tickets soldout” or “limited budget” - and GPT-4 is bad at this! Check out our paper for more information.

  • Jan. 2024: Our paper got accepted to ICLR 2024 as Spotlight! A huge honor, see you in Vienna, Austria!

  • Jan. 2024: New pre-print! We proposes EasyTool that improves your agents’ tool-usage with minimal efforts: by streamlining tool documents into tool instructions 🔨. Check out this tweet!

  • Dec. 2023: Our paper IdiomKB on Idiomatic Translation got accepted to AAAI 2024! We propose a multilingual idiom KB (IdiomKB) developed using LLMs to facilitate better idiomatic translation by smaller models by retrieving idioms’ figurative meanings.

  • Dec. 2023: Social@EMNLP 2023, Singapore, open to any chat!

  • Nov. 2023: Gave a talk at CMU LTI Li Lab, titled: “Say, Think, Act: Towards Human-like Autonomous Language Agents”.

  • Oct. 2023: Check out our newest pre-print Auction Arena! We explore the intriguing domain of how LLMs navigate the complex and dynamic environment of auctions. We introduce AucArena, a novel simulation environment tailored for assessing LLMs within the unpredictable yet strategic world of auctions. Play with arena demo and see if you can beat AI!

  • Oct. 2023: Our paper SCAR got accepted to EMNLP 2023 Findings! A nice addition to the analogical reasoning domain! See you in Singapore :).

  • July 2023: Our paper CoScript got an Outstanding Paper Award in ACL 2023!

  • June 2023: Coming to Seattle for a summer internship at Allen Institute for AI, working with the great Aristo Team!

  • May 2023: A pre-print on the knowledge conflict of large language models! See the tweet. Turns out ChatGPT and GPT-4 somehow stick to its own belief, are receptive/gullible to longer, better-formatted, more popular evidence, and follow the herd… All kinds of human-like, dangerous behaviors!

  • May 2023: Check out two pre-prints on Analogical Reasoning, which extend E-KAR! AnalogyKB is a million-scale analogy KB derived from existing KGs, to enable machines to achieve analogical reasoning skills. SCAR is a new challenge for evaluating LLMs’ structure abduction ability for scientific analogies, which is essential for human-like analogical reasoning.

  • May 2023: Got two papers about LLMs accepted to the main conference of ACL 2023! The first paper is about analyzing why LLMs fail to generate negative knowledge while being able to recognize them. The other is CoScript, studying how to generate plans under constraints with LLMs. See you in Toronto (hopefully :/)!


Jul 2024 – Present Shanghai, China
Allen Institute for AI
Research Intern
Allen Institute for AI
Jun 2023 – Sep 2023 Seattle, Washington, U.S.
Aristo Team, mentored by Dr. Kyle Richardson. Responsibilities: Work on multi-agent reasoning and planning with large language models.
UC Santa Barbara
Visiting Research Intern
UC Santa Barbara
Sep 2021 – May 2023 Remote
Hosted by Prof. Lei Li. Responsibilities: Work on machine reasoning over language with large language models.
ByteDance AI Lab
Research Intern
ByteDance AI Lab
Nov 2019 – May 2023 Shanghai, China
Mentored by Prof. Lei Li, Prof. Hao Zhou, and Dr. Changzhi Sun. Work on Knowledge-guided text generation and natural language reasoning.
Knowledge Works Lab (KW, 知识工场) at Fudan University
Student Research Leader
Knowledge Works Lab (KW, 知识工场) at Fudan University
Sep 2019 – Jun 2024 Shanghai, China
Responsibilities: Lead the research group on natural language generation and reasoning. Mentored near 20 graduate and undergraduate students. Together, we co-authored and published multiple research papers, including one that received an Outstanding Paper Award in ACL 2023 with Siyu Yuan.


Excellent Graduates of Shanghai
Huawei Scholarship
ACL 2023 Outstanding Paper Award
China National Scholarship for Doctoral Students
Honor Student Award in Computer Science of Top Talent Undergraduate Training Program

Recent Publications

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(2024). How Easily do Irrelevant Inputs Skew the Responses of Large Language Models?. COLM 2024.

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(2024). DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?. Preprint.

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(2024). EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction. Preprint.

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(2024). SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals. Preprint.

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(2024). From Persona to Personalization: A Survey on Role-Playing Language Agents. Preprint.

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