---
title: "Weizhu Chen — all works — Elamur Research"
description: "All 37 works of Weizhu Chen in the Elamur Research library, the most cited first: our summaries where we have them, else the original."
canonical: "https://research.elamur.ai/free/en/authors/weizhu-chen/works/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/weizhu-chen/works/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Weizhu Chen](https://md.research.elamur.ai/free/en/authors/weizhu-chen/) / all works

# Weizhu Chen — all works

37 works · [← Weizhu Chen](https://md.research.elamur.ai/free/en/authors/weizhu-chen/)

1. [**LoRA: Low-Rank Adaptation of Large Language Models arXiv:2106.09685**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 8,661 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
2. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
3. **[Multi-Task Deep Neural Networks for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/liu-2019-multi-task-deep-neural-networks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Liu, Pengcheng He, Weizhu Chen, et al.*2019 · 1,524 citations*
4. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
5. [**What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,256 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
6. **[AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://md.research.elamur.ai/free/en/papers/zhong-2024-agieval-human-centric-benchmark-evaluating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wanjun Zhong, Ruixiang Cui, Yiduo Guo, et al.*2024 · 575 citations*
7. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy arXiv:2305.15294**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
8. [**Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback arXiv:2302.12813**Baolin Peng, Michel Galley, Pengcheng He, et al.*2023 · 363 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/)
9. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing arXiv:2305.11738**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 326 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
10. **[SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 304 citations*
11. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor arXiv:2107.07653**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 255 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
12. **[Understanding the Difficulty of Training Transformers](https://md.research.elamur.ai/free/en/papers/liu-2020-understanding-difficulty-training-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liyuan Liu, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 231 citations*
13. [**ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving arXiv:2309.17452**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 225 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-tora-tool-integrated-reasoning-agent/)
14. **[Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 217 citations*
15. [**AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning arXiv:2303.10512**Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/)
16. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators arXiv:2303.16854**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
17. [**Generation-Augmented Retrieval for Open-domain Question Answering arXiv:2009.08553**Yuning Mao, Pengcheng He, Xiaodong Liu, et al.*2020 · 151 citations*](https://md.research.elamur.ai/free/en/papers/mao-2020-generation-augmented-retrieval-open-domain/)
18. **[A Good Prompt Is Worth Millions of Parameters: Low-resource Prompt-based Learning for Vision-Language Models ↗](https://aclanthology.org/2022.acl-long.197/) Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Woojeong Jin et al.*2022 · 149 citations*
19. **[Adversarial Training for Large Neural Language Models ↗](https://arxiv.org/abs/2004.08994) arXiv (Cornell University) · no summary yet**Xiaodong Liu et al.*2020 · 148 citations*
20. **[Generation-Augmented Retrieval for Open-Domain Question Answering ↗](https://aclanthology.org/2021.acl-long.316/) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yet**Yuning Mao et al.*2021 · 145 citations*
21. **[A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 142 citations*
22. **[Few-Shot Named Entity Recognition: An Empirical Baseline Study ↗](https://aclanthology.org/2021.emnlp-main.813/) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · no summary yet**Jiaxin Huang et al.*2021 · 140 citations*
23. **[Controllable Natural Language Generation with Contrastive Prefixes](https://md.research.elamur.ai/free/en/papers/qian-2022-controllable-natural-language-generation-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Qian, Li Dong, Yelong Shen, et al.*2022 · 130 citations*
24. **[RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation ↗](https://arxiv.org/abs/2303.12570) arXiv:2303.12570 · no summary yet**Fengji Zhang et al.*2023 · 119 citations*
25. **[Reinforcement Learning for Reasoning in Large Language Models with One Training Example ↗](https://arxiv.org/abs/2504.20571) arXiv:2504.20571 · no summary yet**Yiping Wang et al.*2025 · 87 citations*
26. **[Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning ↗](https://aclanthology.org/2020.emnlp-main.722/) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yet**Tao Shen et al.*2020 · 87 citations*
27. **[OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering ↗](https://aclanthology.org/2022.naacl-main.68/) Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · no summary yet**Zhengbao Jiang et al.*2022 · 80 citations*
28. **[Beyond ten blue links ↗](https://doi.org/10.1145/2124295.2124351) ACM International Conference on Web Search and Data Mining (WSDM) · no summary yet**Danqi Chen et al.*2012 · 71 citations*
29. **[Learning From Mistakes Makes LLM Better Reasoner ↗](https://arxiv.org/abs/2310.20689) arXiv:2310.20689 · no summary yet**Shengnan An et al.*2023 · 64 citations*
30. [**Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling arXiv:2406.07522**Liliang Ren, Yang Liu, Yadong Lu, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/ren-2024-samba-simple-hybrid-state-space/)
31. **[The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding ↗](https://aclanthology.org/2020.acl-demos.16/) Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · no summary yet**Xiaodong Liu et al.*2020 · 56 citations*
32. **[HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalizability ↗](https://aclanthology.org/2021.acl-long.338/) Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yet**Jiaao Chen et al.*2021 · 49 citations*
33. **[Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning ↗](https://arxiv.org/abs/2403.02333) arXiv:2403.02333 · no summary yet**Yiming Huang et al.*2024 · 45 citations*
34. **[Code Execution with Pre-trained Language Models ↗](https://aclanthology.org/2023.findings-acl.308/) Findings of the Association for Computational Linguistics: ACL 2023 · no summary yet**Chenxiao Liu et al.*2023 · 38 citations*
35. **[Automatic Instruction Evolving for Large Language Models](https://md.research.elamur.ai/free/en/papers/zeng-2024-automatic-instruction-evolving-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weihao Zeng, Can Xu, Yingxiu Zhao, et al.*2024 · 28 citations*
36. **[HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization ↗](https://arxiv.org/abs/2106.00149) arXiv (Cornell University) · no summary yet**Jiaao Chen et al.*2021 · 6 citations*
37. **[Characterizing Inverse Time Dependency in Multi-class Learning ↗](https://doi.org/10.1109/icdm.2011.32) Proceedings · no summary yet**Danqi Chen, Weizhu Chen, Qiang Yang*2011 · 2 citations*