activity
20232025
most citedEmpowering Working Memory for Large Language Model Agents

5 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025★ 1 cited

Spec-Driven AI for Science: The ARIA Framework for Automated and Reproducible Data Analysis

Chuke Chen, Biao Luo, Nan Li +4

The rapid expansion of scientific data has widened the gap between analytical capability and research intent. Existing AI-based analysis tools, ranging from AutoML frameworks to ag…

cs.CL2025★ 3 cited

Environmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain

Jing Guo, Nan Li, Ming Xu

Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness…

cs.DB2024★ 1 cited

A Method for Parsing and Vectorization of Semi-structured Data used in Retrieval Augmented Generation

Hang Yang, Jing Guo, Jianchuan Qi +5

This paper presents a novel method for parsing and vectorizing semi-structured data to enhance the functionality of Retrieval-Augmented Generation (RAG) within Large Language Model…

cs.CL2024★ 4 cited

Advanced Unstructured Data Processing for ESG Reports: A Methodology for Structured Transformation and Enhanced Analysis

Jiahui Peng, Jing Gao, Xin Tong +6

In the evolving field of corporate sustainability, analyzing unstructured Environmental, Social, and Governance (ESG) reports is a complex challenge due to their varied formats and…

cs.CL2023★ 5 cited

Empowering Working Memory for Large Language Model Agents

Jing Guo, Nan Li, Jianchuan Qi +5

Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constra…