Papers by Jiaxiang Ren
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models (2024.emnlp-main)
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| Challenge: | Existing frameworks for learning Large Language Models (LLMs) require adaptive data processing and low-rank adjustment to improve accuracy and fine-tuning speed. |
| Approach: | They propose a fisher information-based adaptive federated curriculum learning framework with two novel methods to improve FL fine-tuning process. |
| Outcome: | The proposed framework improves performance and fine-tuning speed compared with baseline approaches. |
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)
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| Challenge: | Prompt tuning of Large Language Models (LLMs) can incur performance degradation or low training efficiency. |
| Approach: | They propose a prompt tuning approach with Adaptive Optimization to enable efficient FL of LLMs. |
| Outcome: | The proposed approach improves performance and efficiency simultaneously and addresses client drift problems on both the device and server sides. |
Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement (2026.eacl-industry)
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Aaditya Shukla, Sidney Knowles, Meenakshi Madugula, David Farris, Ryan Angilly, Santiago Pombo, Lu An, Anbang Xu, Abhinav Balasubramanian, Tan Yu, Jiaxiang Ren, Rama Akkiraju
| Challenge: | NVInfo AI is a generative AI agent that can be deployed in production without full-scale retraining or infrastructure overhauls. |
| Approach: | They propose to implement a retrieval-augmented generation (RAG)-driven data flywheel in NVInfo AI, a mixture-of-experts knowledge assistant, for 30,000 employees. |
| Outcome: | The proposed system addresses failures in retrieval-augmented generation pipelines and enables continuous learning. |