Papers by Na Zou
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation (2025.acl-long)
Copied to clipboard
Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan, Chin-Chia Michael Yeh, Guanchu Wang, Mingzhi Hu, Zhichao Xu, Yan Zheng, Mahashweta Das, Na Zou
| Challenge: | Existing RAG systems struggle with the quality of retrieval documents, causing performance degradation and reducing performance. |
| Approach: | They propose a training-free RAG framework that leverages multiple LLM agents to collaboratively filter and score retrieved documents. |
| Outcome: | The proposed framework outperforms existing RAG frameworks in QA benchmarks and shows superior answer consistency and answer accuracy over baseline methods. |
Quantized Can Still Be Calibrated: A Unified Framework to Calibration in Quantized Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods to quantify uncertainty of large language models (LLMs) but their influence on uncertainty calibration remains unexplored. |
| Approach: | They propose an analytic method to estimate the upper bound of calibration error (UBCE) for quantized LLMs and propose a method to recover calibration errors through soft-prompt tuning. |
| Outcome: | The proposed method improves the calibration accuracy of quantized models on multiple datasets and LLMs. |