Papers by Zeren Jiang
GeAR: Graph-enhanced Agent for Retrieval-augmented Generation (2025.findings-acl)
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Zhili Shen, Chenxin Diao, Pavlos Vougiouklis, Pascual Merita, Shriram Piramanayagam, Enting Chen, Damien Graux, Andre Melo, Ruofei Lai, Zeren Jiang, Zhongyang Li, Ye Qi, Yang Ren, Dandan Tu, Jeff Z. Pan
| Challenge: | Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers struggle with multi-hop retrieval scenarios. |
| Approach: | They propose a graph expansion mechanism that augments any conventional base retriever and an agent framework that incorporates the resulting graph-based retrieval into a multi-step retrieval framework. |
| Outcome: | The proposed system achieves state-of-the-art results on three multi-hop question answering datasets while consuming fewer tokens and requiring fewer iterations than existing multi-step retrieval systems. |
Crafting Personalized Agents through Retrieval-Augmented Generation on Editable Memory Graphs (2024.emnlp-main)
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| Challenge: | In the age of mobile internet, personal information is constantly being generated on smartphones. |
| Approach: | They propose a novel task of crafting personalized agents powered by large language models that leverage a user's smartphone memories to enhance downstream applications with LLM capabilities. |
| Outcome: | The proposed approach improves 10% over the best existing approach on a real-world dataset and improves usability. |