Fetching the paper…
Reading the bibliography…
As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing.
Codesearchnet challenge: Evaluating the state of semantic code search
H. Husain, H.-H. Wu, T. Gazit, M. Allamanis, and M. Brockschmidt · 1909
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Earlier work this paper cites.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow, 2021
S. Black, L. Gao, P. Wang, C. Leahy, and S. Biderman · 2021
Earlier work this paper cites.
Measuring coding challenge competence with APPS
D. Hendrycks, S. Basart, S. Kadavath, M. Mazeika, A. Arora, E. Guo, C. Burns, S. Puranik, H. He, D. Song, and J. Steinhardt · 2021
Earlier work this paper cites.
Cosqa: 20,000+ web queries for code search and question answering
J. Huang, D. Tang, L. Shou, M. Gong, K. Xu, D. Jiang, M. Zhou, and N. Duan · 2021
Earlier work this paper cites.
CodeXGLUE: A machine learning benchmark dataset for code understanding and generation
S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. Clement, D. Drain, D. Jiang, D. Tang, G. Li, L. Zhou, L. Shou, L. Zhou, M. Tufano, M. GONG, M. Zhou, N. Duan, N. Sundaresan, S. K. Deng, S. Fu, and S. LIU · 2021
Earlier work this paper cites.
GPT-NeoX-20B: An open-source autoregressive language model
S. Black, S. Biderman, E. Hallahan, Q. Anthony, L. Gao, L. Golding, H. He, C. Leahy, K. McDonell, J. Phang, M. Pieler, U. S. Prashanth, S. Purohit, L. Reynolds, J. Tow, B. Wang, and S. Weinbach · 2022
Earlier work this paper cites.
Multipl-e: A scalable and extensible approach to benchmarking neural code generation
F. Cassano, J. Gouwar, D. Nguyen, S. Nguyen, L. Phipps-Costin, D. Pinckney, M.-H. Yee, Y. Zi, C. J. Anderson, M. Q. Feldman, et al · 2022
Earlier work this paper cites.
Cocomic: Code completion by jointly modeling in-file and cross-file context
Y. Ding, Z. Wang, W. U. Ahmad, M. K. Ramanathan, R. Nallapati, P. Bhatia, D. Roth, and B. Xiang · 2022
Earlier work this paper cites.
Review4repair: Code review aided automatic program repairing
F. Huq, M. Hasan, M. M. A. Haque, S. Mahbub, A. Iqbal, and T. Ahmed · 2022
Earlier work this paper cites.
Ds-1000: A natural and reliable benchmark for data science code generation
Y. Lai, C. Li, Y. Wang, T. Zhang, R. Zhong, L. Zettlemoyer, W.-T. Yih, D. Fried, S. Wang, and T. Yu · 2022
Earlier work this paper cites.
Coderl: Mastering code generation through pretrained models and deep reinforcement learning
H. Le, Y. Wang, A. D. Gotmare, S. Savarese, and S. C. H. Hoi · 2022
Earlier work this paper cites.
Competition-level code generation with alphacode
Y. Li, D. Choi, J. Chung, N. Kushman, J. Schrittwieser, R. Leblond, T. Eccles, J. Keeling, F. Gimeno, A. D. Lago, et al · 2022
Earlier work this paper cites.
ReACC: A retrieval-augmented code completion framework
S. Lu, N. Duan, H. Han, D. Guo, S.-w. Hwang, and A. Svyatkovskiy · 2022
Earlier work this paper cites.
Tssb-3m: Mining single statement bugs at massive scale
C. Richter and H. Wehrheim · 2022
Earlier work this paper cites.
A systematic evaluation of large language models of code
F. F. Xu, U. Alon, G. Neubig, and V. J. Hellendoorn · 2022
Earlier work this paper cites.
Minif2f: a cross-system benchmark for formal olympiad-level mathematics, 2022
K. Zheng, J. M. Han, and S. Polu · 2022
Earlier work this paper cites.
Xlcost: A benchmark dataset for cross-lingual code intelligence
M. Zhu, A. Jain, K. Suresh, R. Ravindran, S. Tipirneni, and C. K. Reddy · 2022
Earlier work this paper cites.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
Earlier work this paper cites.
Guiding language models of code with global context using monitors
L. A. Agrawal, A. Kanade, N. Goyal, S. K. Lahiri, and S. K. Rajamani · 2023
Earlier work this paper cites.
Santacoder: don’t reach for the stars!
L. B. Allal, R. Li, D. Kocetkov, C. Mou, C. Akiki, C. M. Ferrandis, N. Muennighoff, M. Mishra, A. Gu, M. Dey, et al · 2023
Cited alongside, same era.
Multi-lingual evaluation of code generation models
B. Athiwaratkun, S. K. Gouda, Z. Wang, X. Li, Y. Tian, M. Tan, W. U. Ahmad, S. Wang, Q. Sun, M. Shang, S. K. Gonugondla, H. Ding, V. Kumar, N. Fulton, A. Farahani, S. Jain, R. Giaquinto, H. Qian, M. K. Ramanathan, R. Nallapati, B. Ray, P. Bhatia, S. Sengupta, D. Roth, and B. Xiang · 2023
Cited alongside, same era.
J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang, et al · 2023
Cited alongside, same era.
Codeplan: Repository-level coding using llms and planning
R. Bairi, A. Sonwane, A. Kanade, A. Iyer, S. Parthasarathy, S. Rajamani, B. Ashok, S. Shet, et al · 2023
Cited alongside, same era.
Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Q. Zheng, X. Xia, X. Zou, Y. Dong, S. Wang, Y. Xue, Z. Wang, L. Shen, A. Wang, Y. Li, T. Su, Z. Yang, and J. Tang · 2023
Later among the works it cites.
Mceval: Massively multilingual code evaluation
L. Chai, S. Liu, J. Yang, Y. Yin, K. Jin, J. Liu, T. Sun, G. Zhang, C. Ren, H. Guo, et al · 2024
Closest in time.
J. Dai, J. Lu, Y. Feng, D. Huang, G. Zeng, R. Ruan, M. Cheng, H. Tan, and Z. Guo · 2024
Closest in time.
K. Deng, J. Liu, H. Zhu, C. Liu, J. Li, J. Wang, P. Zhao, C. Zhang, Y. Wu, X. Yin, Y. Zhang, W. Su, B. Xiang, T. Ge, and B. Zheng · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Cassano, J. Gouwar, D. Nguyen, S. Nguyen, L. Phipps-Costin, D. Pinckney, M.-H. Yee, Y. Zi, C. J. Anderson, M. Q. Feldman, A. Guha, M. Greenberg, and A. Jangda · 2023
Cited alongside, same era.
Palm: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al · 2023
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
D. Fried, A. Aghajanyan, J. Lin, S. Wang, E. Wallace, F. Shi, R. Zhong, S. Yih, L. Zettlemoyer, and M. Lewis · 2023
Cited alongside, same era.
Pal: Program-aided language models
L. Gao, A. Madaan, S. Zhou, U. Alon, P. Liu, Y. Yang, J. Callan, and G. Neubig · 2023
Cited alongside, same era.
On the evaluation of neural code translation: Taxonomy and benchmark
M. Jiao, T. Yu, X. Li, G. Qiu, X. Gu, and B. Shen · 2023
Cited alongside, same era.
xcodeeval: A large scale multilingual multitask benchmark for code understanding, generation, translation and retrieval, 2023
M. A. M. Khan, M. S. Bari, X. L. Do, W. Wang, M. R. Parvez, and S. Joty · 2023
Cited alongside, same era.
Efficient memory management for large language model serving with pagedattention
W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. Gonzalez, H. Zhang, and I. Stoica · 2023
Cited alongside, same era.
DS-1000: A natural and reliable benchmark for data science code generation
Y. Lai, C. Li, Y. Wang, T. Zhang, R. Zhong, L. Zettlemoyer, W. Yih, D. Fried, S. I. Wang, and T. Yu · 2023
Cited alongside, same era.
S. Dou, J. Zhang, J. Zang, Y. Tao, W. Zhou, H. Jia, S. Liu, Y. Yang, Z. Xi, S. Wu, S. Zhang, M. Wu, C. Lv, L. Xiong, W. Zhan, L. Zhang, R. Weng, J. Wang, X. Cai, Y. Wu, M. Wen, R. Zheng, T. Ji, Y. Cao, T. Gui, X. Qiu, Q. Zhang, and X. Huang · 2024
Closest in time.
Cruxeval: A benchmark for code reasoning, understanding and execution
A. Gu, B. Rozière, H. Leather, A. Solar-Lezama, G. Synnaeve, and S. I. Wang · 2024
Closest in time.
Qwen2. 5-coder technical report
B. Hui, J. Yang, Z. Cui, J. Yang, D. Liu, L. Zhang, T. Liu, J. Zhang, B. Yu, K. Dang, et al · 2024
Closest in time.
Livecodebench: Holistic and contamination free evaluation of large language models for code
N. Jain, K. Han, A. Gu, W.-D. Li, F. Yan, T. Zhang, S. Wang, A. Solar-Lezama, K. Sen, and I. Stoica · 2024
Closest in time.
Difysandbox
LangGenius · 2024
Closest in time.
Procqa: A large-scale community-based programming question answering dataset for code search
Z. Li, J. Zhang, C. Yin, Y. Ouyang, and W. Rong · 2024
Closest in time.
Starcoder 2 and the stack v2: The next generation
A. Lozhkov, R. Li, L. B. Allal, F. Cassano, J. Lamy-Poirier, N. Tazi, A. Tang, D. Pykhtar, J. Liu, Y. Wei, et al · 2024
Closest in time.
Self-refine: Iterative refinement with self-feedback
A. Madaan, N. Tandon, P. Gupta, S. Hallinan, L. Gao, S. Wiegreffe, U. Alon, N. Dziri, S. Prabhumoye, Y. Yang, et al · 2024
Closest in time.
Tablegpt2: A large multimodal model with tabular data integration
A. Su, A. Wang, C. Ye, C. Zhou, G. Zhang, G. Zhu, H. Wang, H. Xu, H. Chen, H. Li, et al · 2024
Closest in time.
Unicoder: Scaling code large language model via universal code
T. Sun, L. Chai, Y. Y. Jian Yang, H. Guo, J. Liu, B. Wang, L. Yang, and Z. Li · 2024
Closest in time.
The llama 3 herd of models
L. Team · 2024
Closest in time.
Debugbench: Evaluating debugging capability of large language models
R. Tian, Y. Ye, Y. Qin, X. Cong, Y. Lin, Z. Liu, and M. Sun · 2024
Closest in time.
Tablebench: A comprehensive and complex benchmark for table question answering
X. Wu, J. Yang, L. Chai, G. Zhang, J. Liu, X. Du, D. Liang, D. Shu, X. Cheng, T. Sun, et al · 2024
Closest in time.
Yi: Open foundation models by 01. ai
A. Young, B. Chen, C. Li, C. Huang, G. Zhang, G. Zhang, H. Li, J. Zhu, J. Chen, J. Chang, et al · 2024
Closest in time.
Codereval: A benchmark of pragmatic code generation with generative pre-trained models
H. Yu, B. Shen, D. Ran, J. Zhang, Q. Zhang, Y. Ma, G. Liang, Y. Li, Q. Wang, and T. Xie · 2024
Closest in time.
Naturalcodebench: Examining coding performance mismatch on humaneval and natural user prompts, 2024
S. Zhang, H. Zhao, X. Liu, Q. Zheng, Z. Qi, X. Gu, X. Zhang, Y. Dong, and J. Tang · 2024
Closest in time.
Codegemma: Open code models based on gemma
C. T. H. Zhao, J. Hui, J. Howland, N. Nguyen, S. Zuo, A. Hu, C. A. Choquette-Choo, J. Shen, J. Kelley, K. tij Bansal, L. Vilnis, M. Wirth, P. Michel, P. Choy, P. Joshi, R. Kumar, S. Hashmi, S. Agrawal, Z. Gong, J. Fine, T. B. Warkentin, A. J. Hartman, B. Ni, K. Korevec, K. Schaefer, and S. Huffman · 2024
Closest in time.