Jian Pei

About

Jian Pei is a professor in the field of computer science with research interests that include large language model (LLM)-based agents. His work focuses on the generalizability of these agents, which refers to their ability to maintain consistently high performance across varied instructions, tasks, environments, and domains, especially those different from the agent’s fine-tuning data. Pei has contributed to advancing the understanding of generalizability by providing comprehensive reviews that clarify its definition and boundaries, review existing benchmarks, and categorize strategies for improving generalizability. These strategies include methods targeting the backbone LLM, agent components, and their interactions. Furthermore, his research distinguishes between generalizable frameworks and generalizable agents, outlining how frameworks can be translated into agent-level generalizability. Pei’s work aims to establish a foundation for principled research on building LLM-based agents that generalize reliably across diverse real-world applications, identifying future directions such as standardized evaluation frameworks, variance- and cost-based metrics, and hybrid approaches integrating methodological innovations with agent architecture-level designs.

Research topics

• Computer Science
• Data Mining
• Artificial Intelligence
• Machine Learning
• Data science

Selected publications

• Data Mining Concepts and Techniques Third Edition

  2021 · 1198 citations

  Senior authorCorresponding

• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey

  ACM Computing Surveys · 2025-09-03 · 41 citations

  articleOpen access

  Large language models (LLMs) have significantly advanced the field of natural language processing (NLP), providing a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles, caused by the heterogeneity of domain data, the sophistication of domain knowledge, the uniqueness of domain objectives, and the diversity of the constraints (e.g., various social norms, cultural conformit…

  PublisherOA PDFDOI

• Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

  ArXiv.org · 2025-03-31 · 14 citations

  preprintOpen access

  The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. As these agents increasingly drive AI research and practical applications, their design, evaluation, and continuous improvement present intricate, multifaceted challenges. This book provides a comprehensive overview, framing intelligent agents w…

  PublisherOA PDFDOI

• A Survey on Small Language Models in the Era of Large Language Models: Architecture, Capabilities, and Trustworthiness

  2025-08-03 · 5 citations

  articleOpen access

  Large language models (LLMs) based on Transformer architecture are powerful but face challenges with deployment, inference latency, and costly fine-tuning. These limitations highlight the emerging potential of small language models (SLMs), which can either replace LLMs through innovative architectures and technologies, or assist them as efficient proxy or reward models. Emerging architectures such as Mamba and xLSTM address the quadratic scaling of inference with window length in Transformers by…

  PublisherOA PDFDOI

• Shapley Value Estimation based on Differential Matrix

  Proceedings of the ACM on Management of Data · 2025-02-10 · 1 citations

  article

  The Shapley value has been extensively used in many fields as the unique metric to fairly evaluate player contributions in cooperative settings. Since the exact computation of Shapley values is \#P-hard in the task-agnostic setting, many studies have been developed to utilize the Monte Carlo method for Shapley value estimation. The existing methods estimate the Shapley values directly. In this paper, we explore a novel idea-inferring the Shapley values by estimating the differences between them.…

  PublisherDOI

Frequent coauthors

• Yang Yu

  666 shared

• Enhong Chen

  University of Science and Technology of China

  666 shared

• Zhi‐Hua Zhou

  Nanjing University

  652 shared

• João Gama

  INESC TEC

  651 shared

• Chengqi Zhang

  651 shared

• Geoffrey I. Webb

  650 shared

• Hiroshi Motoda

  Osaka University

  650 shared

• Jaideep Srivastava

  649 shared

Education

• Ph.D., Computer Science

  University of California, Berkeley

  1994

• M.S., Computer Science

  University of California, Berkeley

  1991

• B.S., Computer Science

  University of Science and Technology of China

  1988

Awards & honors

• IEEE Fellow

Similar researchers at Duke University

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• Christopher Monroeh-140
• Michael Reiterh-83