Papers by Jiefeng Chen
DocLens: A Tool-Augmented Multi-Agent Framework for Long Visual Document Understanding (2026.acl-long)
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| Challenge: | Existing approaches to localizing evidence from long visual documents fail on a fundamental challenge: evidence localization. |
| Approach: | They propose a tool-augmented multi-agent framework that “zooms in” on evidence like a lens. |
| Outcome: | The proposed framework achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, surpassing even human experts. |
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models (2025.acl-long)
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| Challenge: | Existing studies have not linked the behavior of retrieval augmented generation (RAG) with imperfect retrieval, including irrelevant, misleading, or even malicious information. |
| Approach: | They propose an approach that integrates external knowledge with source-awareness to overcome imperfect retrieval errors in RAG. |
| Outcome: | The proposed approach is superior to previous robustness-enhanced approaches under the worst-case scenario. |
Adaptation with Self-Evaluation to Improve Selective Prediction in LLMs (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown impressive capabilities in many tasks, including natural language understanding and generation. |
| Approach: | They propose a framework for adaptation with self-evaluation to improve selective prediction performance of large language models. |
| Outcome: | The proposed framework outperforms state-of-the-art selective prediction methods on QA datasets and improves the AUACC from 91.23% to 92.63% and AUROC from 74.61% to 80.25%. |