---
title: "Who cites Zilin Zhang"
description: "622 papers of the Elamur Research library cite the works of Zilin Zhang, found in their bibliographies; the most cited first."
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---
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# Who cites Zilin Zhang

667 citations of their works from 622 papers of our library (of the 140,884 whose bibliographies we read) · 41 with a page here, the most cited first · [← Zilin Zhang](https://md.research.elamur.ai/free/en/authors/zilin-zhang/)

1. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
2. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
3. [**Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task**Tao Yu, Rui Zhang, Kai Yang, et al.*2018 · 1,582 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/)
4. [**TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data**Pengcheng Yin, Graham Neubig, Wen-tau Yih, et al.*2020 · 690 citations*](https://md.research.elamur.ai/free/en/papers/yin-2020-tabert-pretraining-joint-understanding-textual/)
5. [**Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation**Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, et al.*2023 · 632 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-your-code-generated-chatgpt-really/)
6. [**SWE-bench: Can Language Models Resolve Real-World GitHub Issues?**Carlos E. Jimenez, John Yang, Alexander Wettig, et al.*2024 · 617 citations*](https://md.research.elamur.ai/free/en/papers/jimenez-2024-swe-bench-can-language-models/)
7. [**Qwen2.5-Coder Technical Report**Binyuan Hui, Jian Yang, Zeyu Cui, et al.*2024 · 547 citations*](https://md.research.elamur.ai/free/en/papers/hui-2024-qwen2-5-coder-technical-report/)
8. [**The Natural Language Decathlon: Multitask Learning as Question Answering**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
9. [**FinQA: A Dataset of Numerical Reasoning over Financial Data**Zhiyu Chen, Wenhu Chen, Charese Smiley, et al.*2021 · 510 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-finqa-dataset-numerical-reasoning-over/)
10. [**TabFact: A Large-scale Dataset for Table-based Fact Verification**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 508 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
11. [**Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation**Dawei Gao, Haibin Wang, Yaliang Li, et al.*2023 · 461 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-text-sql-empowered-large-language/)
12. [**Measuring Coding Challenge Competence With APPS**Dan Hendrycks, Steven Basart, Saurav Kadavath, et al.*2021 · 438 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-coding-challenge-competence-apps/)
13. [**Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs**Jinyang Li, Binyuan Hui, Ge Qu, et al.*2023 · 419 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-llm-already-serve-database-interface/)
14. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
15. [**DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction**Mohammadreza Pourreza, Davood Rafiei*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/pourreza-2023-din-sql-decomposed-context-learning/)
16. [**TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance**Fengbin Zhu, Wenqiang Lei, Youcheng Huang, et al.*2021 · 396 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2021-tat-qa-question-answering-benchmark/)
17. [**Mind2Web: Towards a Generalist Agent for the Web**Xiang Deng, Yu Gu, Boyuan Zheng, et al.*2023 · 376 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-mind2web-towards-generalist-agent-web/)
18. [**HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data**Wenhu Chen, Hanwen Zha, Zhiyu Chen, et al.*2020 · 376 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-hybridqa-dataset-multi-hop-question/)
19. [**A Survey on Large Language Models for Code Generation**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
20. [**RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL**Haoyang Li, Jing Zhang, Cuiping Li, et al.*2023 · 277 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-resdsql-decoupling-schema-linking-skeleton/)
21. [**Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing**Ben Bogin, Jonathan Berant, Matt Gardner*2019 · 272 citations*](https://md.research.elamur.ai/free/en/papers/bogin-2019-representing-schema-structure-graph-neural/)
22. [**CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases**Tao Yu, Rui Zhang, Heyang Er, et al.*2019 · 261 citations*](https://md.research.elamur.ai/free/en/papers/yu-2019-cosql-conversational-text-sql-challenge/)
23. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 254 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
24. [**RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers**Bailin Wang, Richard Shin, Xiaodong Liu, et al.*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-rat-sql-relation-aware-schema/)
25. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 237 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
26. [**A Discrete Hard EM Approach for Weakly Supervised Question Answering**Sewon Min, Danqi Chen, Hannaneh Hajishirzi, et al.*2019 · 233 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/)
27. [**SParC: Cross-Domain Semantic Parsing in Context**Tao Yu, Rui Zhang, Michihiro Yasunaga, et al.*2019 · 227 citations*](https://md.research.elamur.ai/free/en/papers/yu-2019-sparc-cross-domain-semantic-parsing/)
28. [**Break It Down: A Question Understanding Benchmark**Tomer Wolfson, Mor Geva, Ankit Gupta, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/)
29. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
30. [**Large Language Models are few(1)-shot Table Reasoners**Wenhu Chen*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/)
31. [**Structure-Grounded Pretraining for Text-to-SQL**Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek, et al.*2021 · 166 citations*](https://md.research.elamur.ai/free/en/papers/deng-2021-structure-grounded-pretraining-text-sql/)
32. [**Binding Language Models in Symbolic Languages**Zhoujun Cheng, Tianbao Xie, Peng Shi, et al.*2022 · 165 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2022-binding-language-models-symbolic-languages/)
33. [**SantaCoder: don't reach for the stars!**Loubna Ben Allal, Raymond Li, Denis Kocetkov, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/allal-2023-santacoder-don-t-reach-stars/)
34. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 145 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
35. [**LEVER: Learning to Verify Language-to-Code Generation with Execution**Ansong Ni, Srini Iyer, Dragomir Radev, et al.*2023 · 122 citations*](https://md.research.elamur.ai/free/en/papers/ni-2023-lever-learning-verify-language-code/)
36. [**Multi-VALUE: A Framework for Cross-Dialectal English NLP**Caleb Ziems, William Held, Jingfeng Yang, et al.*2023 · 73 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-multi-value-framework-cross-dialectal/)
37. [**DuSQL: A Large-Scale and Pragmatic Chinese Text-to-SQL Dataset**Lijie Wang, Ao Zhang, Kun Wu, et al.*2020 · 63 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-dusql-large-scale-pragmatic-chinese/)
38. [**Ar-Spider: Text-to-SQL in Arabic**Saleh Almohaimeed, Saad Almohaimeed, Mansour Al Ghanim, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/almohaimeed-2024-ar-spider-text-sql-arabic/)
39. [**Prompt Engineering Techniques for Context-dependent Text-to-SQL in Arabic**Saleh Almohaimeed, May Alsofyani, Saad Almohaimeed, et al.*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/almohaimeed-2025-prompt-engineering-techniques-context-dependent/)
40. [**AraSpider: Democratizing Arabic-to-SQL**Ahmed Heakl; Youssef Mohamed;, Ahmed B. Zaky*2024 · 1 citation*](https://md.research.elamur.ai/free/en/papers/heakl-2024-araspider-democratizing-arabic-sql/)
41. [**Questionnaire Meets LLM: A Benchmark and Empirical Study of Structural Skills for Understanding Questions and Responses**Duc-Hai Nguyen, Vijayakumar Nanjappan, Barry O'Sullivan, et al.*2026*](https://md.research.elamur.ai/free/en/papers/nguyen-2026-questionnaire-meets-llm-benchmark-empirical/)

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