Lida Chen

Lida Chen

Ph.D Candidate

Fudan University

Biography

Lida Chen (陈立达) is a second year Ph.D candidate at Fudan University in the School of Computer Science. His interested research topics are mostly around long context models, including (but are not limited to):

  1. Long-Context LLMs: Aiming to enable LLMs to efficiently and effectively process extremely long inputs, reducing the deployment cost of 1M-context production-level transformers to be as affordable as 4K models. This involves integrating techniques such as long-term memory mechanisms, sparse attention, and KV cache compression, among others, into LLMs.
  2. Video Language Models: Improving long video understanding capabilities of multi-modal large language models (MLLMs). This involves building datasets for long video understanding, developing vision language connectors for lossless compression of video features, and creating efficient model frameworks for processing long videos.
Interests
  • Large Language Models
  • Long Context Models
  • Video Language Models
  • Chinese Chess, Table Tennis
Education
  • Ph.D in NLP, 2023 - 2028 (estimated)

    Fudan University

  • Second Major in Economics, 2020 - 2023

    Fudan University

  • B.S in CS (honors), 2019 - 2023

    Fudan University

Experience

 
 
 
 
 
Knowledge Works Lab, at Fudan University
Student Researcher
Knowledge Works Lab, at Fudan University
September 2023 – Present Shanghai, China
Responsibilities: Worked on long-context models.
 
 
 
 
 
Ant Group
Research Intern
Ant Group
August 2023 – August 2024 Shanghai, China
Work on mitigating hallucinations in Large Language Models.
 
 
 
 
 
Fudan University
ACM Competition Team Member
Fudan University
December 2021 – June 2023 Shanghai, China

Awards

Honor Student Award in Computer Science of Top Talent Undergraduate Training Program
Huawei Scholarship
Second Prize of Computer System Development Capability Competition (Compiler Track)

Recent Publications

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(2024). Teaching Large Language Models to Express Knowledge Boundary from Their Own Signals. In Arxiv.

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(2024). SurveyAgent: A Conversational System for Personalized and Efficient Research Survey. In Arxiv.

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

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