--- title: "Computer Science Applications — papers" description: "Computer Science Applications: 39 papers of the Elamur Research library · page 1 of 2 · 1–25, the most cited first — our summary of each, its ideas…" canonical: "https://research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) / Computer Science Applications # 🖥️ Computer Science Applications 39 papers · page 1 of 2 · 1–25 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Computer Science Applications is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Mobile Crowdsensing and Crowdsourcing», «Teaching and Learning Programming», «Online Learning and Analytics». ## Papers **1** [2](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/page/2/) 1. [**Competition-Level Code Generation with AlphaCode**Yujia Li, David Choi, Junyoung Chung, et al.*2022 · 1,543 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/) 2. [**Code Llama: Open Foundation Models for Code**Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.*2024 · 1,399 citations*](https://md.research.elamur.ai/free/en/papers/roziere-2024-code-llama-open-foundation-models/) 3. [**Program Synthesis with Large Language Models**Jacob Austin, Augustus Odena, Maxwell Nye, et al.*2021 · 1,136 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-program-synthesis-large-language-models/) 4. [**A New Dataset and Method for Automatically Grading ESOL Texts**Helen Yannakoudakis, Ted Briscoe, Ben Medlock*2011 · 813 citations*](https://md.research.elamur.ai/free/en/papers/yannakoudakis-2011-new-dataset-method-automatically-grading/) 5. [**UniXcoder: Unified Cross-Modal Pre-training for Code Representation**Daya Guo, Shuai Lu, Nan Duan, et al.*2022 · 768 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-unixcoder-unified-cross-modal-pre/) 6. [**Unified Pre-training for Program Understanding and Generation**Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, et al.*2021 · 746 citations*](https://md.research.elamur.ai/free/en/papers/ahmad-2021-unified-pre-training-program-understanding/) 7. [**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/) 8. [**Collecting Image Annotations Using Amazon's Mechanical Turk**Cyrus Rashtchian, Peter Young, Micah Hodosh, et al.*2010 · 562 citations*](https://md.research.elamur.ai/free/en/papers/rashtchian-2010-collecting-image-annotations-amazon-s/) 9. [**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/) 10. [**LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code**Naman Jain, King Han, Alex Gu, et al.*2024 · 540 citations*](https://md.research.elamur.ai/free/en/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/) 11. [**CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X**Qinkai Zheng, Xiao Xia, Xu Zou, et al.*2023 · 462 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-codegeex-pre-trained-model-code/) 12. [**Cheap and Fast – But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks**Rion Snow, Brendan O'Connor, Daniel Jurafsky, et al.*2008 · 390 citations*](https://md.research.elamur.ai/free/en/papers/snow-2008-cheap-fast-but-is-it/) 13. [**Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation**Lora M. Aroyo, Chris Welty*2015 · 385 citations*](https://md.research.elamur.ai/free/en/papers/aroyo-2015-truth-lie-crowd-truth-seven/) 14. [**Crowdsourcing Translation: Professional Quality from Non-Professionals**Omar F. Zaidan, Chris Callison-Burch*2011 · 374 citations*](https://md.research.elamur.ai/free/en/papers/zaidan-2011-crowdsourcing-translation-professional-quality-non/) 15. [**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/) 16. [**GraphCodeBERT: Pre-training Code Representations with Data Flow**Daya Guo, Shuo Ren, Shuai Lu, et al.*2021 · 328 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/) 17. [**Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow**Pengcheng Yin, Bowen Deng, Edgar Chen, et al.*2018 · 306 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-learning-mine-aligned-code-natural/) 18. [**CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning**Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al.*2022 · 290 citations*](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/) 19. [**The Benefits of a Model of Annotation**Rebecca J. Passonneau, Bob Carpenter*2014 · 238 citations*](https://md.research.elamur.ai/free/en/papers/passonneau-2014-benefits-model-annotation/) 20. [**Toward a Perspectivist Turn in Ground Truthing for Predictive Computing**Valerio Basile, Federico Cabitza, Andrea Campagner, et al.*2021 · 225 citations*](https://md.research.elamur.ai/free/en/papers/basile-2021-toward-perspectivist-turn-ground-truthing/) 21. [**CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution**Alex Gu, Baptiste Rozière, Hugh Leather, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-cruxeval-benchmark-code-reasoning-understanding/) 22. [**Outsourcing FrameNet to the Crowd**Marco Fossati, Claudio Giuliano, Sara Tonelli*2013 · 44 citations*](https://md.research.elamur.ai/free/en/papers/fossati-2013-outsourcing-framenet-crowd/) 23. [**xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval**Mohammad Abdullah Matin Khan, M Saiful Bari, Xuan Long Do, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/khan-2023-xcodeeval-large-scale-multilingual-multitask/) 24. [**Phrase Detectives Corpus 1.0 Crowdsourced Anaphoric Coreference.**Jon Chamberlain, Massimo Poesio, Udo Kruschwitz*2016 · 31 citations*](https://md.research.elamur.ai/free/en/papers/chamberlain-2016-phrase-detectives-corpus-1-0/) 25. [**A Probabilistic Annotation Model for Crowdsourcing Coreference**Silviu Paun, Jon Chamberlain, Udo Kruschwitz, et al.*2018 · 25 citations*](https://md.research.elamur.ai/free/en/papers/paun-2018-probabilistic-annotation-model-crowdsourcing-coreference/) **1** [2](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/page/2/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-science-applications/page/2/) Updated 23 October 2026 - [← previous subfield Computer Networks and Communications](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-networks-and-communications/) - [next subfield → Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) - [↑ back to Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/)