Fetching the paper…
Reading the bibliography…
The integration of Large Language Models (LLMs) into Development Environments (IDEs) has become a focal point in modern software development.
Bleu: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., and Zhu, W.-J · 2002
Earlier work this paper cites.
Recurrent neural network based language modeling in meeting recognition
Kombrink, S., Mikolov, T., Karafiát, M., and Burget, L · 2011
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Earlier work this paper cites.
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Earlier work this paper cites.
Glue: A multi-task benchmark and analysis platform for natural language understanding, 2019
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., and Bowman, S. R · 2019
Earlier work this paper cites.
Gpt-3: Its nature, scope, limits, and consequences
Floridi, L., and Chiriatti, M · 2020
Earlier work this paper cites.
How context affects language models’ factual predictions, 2020
Petroni, F., Lewis, P., Piktus, A., Rocktäschel, T., Wu, Y., Miller, A. H., and Riedel, S · 2020
Earlier work this paper cites.
Codebleu: a method for automatic evaluation of code synthesis
Ren, S., Guo, D., Lu, S., Zhou, L., Liu, S., Tang, D., Sundaresan, N., Zhou, M., Blanco, A., and Ma, S · 2020
Earlier work this paper cites.
Unsupervised translation of programming languages
Roziere, B., Lachaux, M.-A., Chanussot, L., and Lample, G · 2020
Earlier work this paper cites.
Making pre-trained language models better few-shot learners, 2021
Gao, T., Fisch, A., and Chen, D · 2021
Earlier work this paper cites.
Measuring massive multitask language understanding, 2021
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
Earlier work this paper cites.
Codexglue: A machine learning benchmark dataset for code understanding and generation, 2021
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., Li, G., Zhou, L., Shou, L., Zhou, L., Tufano, M., Gong, M., Zhou, M., Duan, N., Sundaresan, N., Deng, S. K., Fu, S., and Liu, S · 2021
Earlier work this paper cites.
Palm: Scaling language modeling with pathways, 2022
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., and Others · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al · 2022
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher, 2022
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., and Others · 2022
Cited alongside, same era.
Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model, 2022
Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., Zhang, E., Child, R., Aminabadi, R. Y., Bernauer, J., Song, X., Shoeybi, M., He, Y., Houston, M., Tiwary, S., and Catanzaro, B · 2022
Cited alongside, same era.
A survey on evaluation of large language models, 2023
Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., Ye, W., Zhang, Y., Chang, Y., Yu, P. S., Yang, Q., and Xie, X · 2023
Lost in the middle: How language models use long contexts, 2023
Liu, N. F., Lin, K., Hewitt, J., Paranjape, A., Bevilacqua, M., Petroni, F., and Liang, P · 2023
Later among the works it cites.
In-ide generation-based information support with a large language model, 2023
Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., and Myers, B · 2023
Later among the works it cites.
Gpt 3.5 models, 2023
OpenAI · 2023
Later among the works it cites.
Gpt-4 technical report, 2023
OpenAI · 2023
Later among the works it cites.
Is chatgpt a general-purpose natural language processing task solver?, 2023
Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., and Yang, D · 2023
Later among the works it cites.
In-context retrieval-augmented language models, 2023
Ram, O., Levine, Y., Dalmedigos, I., Muhlgay, D., Shashua, A., Leyton-Brown, K., and Shoham, Y · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Vscuda: Llm based cuda extension for visual studio code
Chen, B., Mustakin, N., Hoang, A., Fuad, S., and Wong, D · 2023
Cited alongside, same era.
Exploring the use of large language models for reference-free text quality evaluation: An empirical study, 2023
Chen, Y., Wang, R., Jiang, H., Shi, S., and Xu, R · 2023
Cited alongside, same era.
Gptscore: Evaluate as you desire, 2023
Fu, J., Ng, S.-K., Jiang, Z., and Liu, P · 2023
Cited alongside, same era.
Aligning ai with shared human values, 2023
Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., and Steinhardt, J · 2023
Cited alongside, same era.
Large language models for software engineering: A systematic literature review
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., and Wang, H · 2023
Cited alongside, same era.
A systematic study and comprehensive evaluation of chatgpt on benchmark datasets, 2023
Laskar, M. T. R., Bari, M. S., Rahman, M., Bhuiyan, M. A. H., Joty, S., and Huang, J. X · 2023
Cited alongside, same era.
Holistic evaluation of language models, 2023
Liang, P., Bommasani, R., Lee, T., Tsipras, D., and Others · 2023
Cited alongside, same era.
Code llama: Open foundation models for code
Roziere, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Remez, T., Rapin, J., et al · 2023
Later among the works it cites.
Hierarchical prompting assists large language model on web navigation, 2023
Sridhar, A., Lo, R., Xu, F. F., Zhu, H., and Zhou, S · 2023
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models, 2023
Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., et al · 2023
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models, 2023
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., and Zhou, D · 2023
Later among the works it cites.
Wider and deeper llm networks are fairer llm evaluators, 2023
Zhang, X., Yu, B., Yu, H., Lv, Y., Liu, T., Huang, F., Xu, H., and Li, Y · 2023
Later among the works it cites.
Lima: Less is more for alignment, 2023
Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., Zhang, S., Ghosh, G., Lewis, M., Zettlemoyer, L., and Levy, O · 2023
Later among the works it cites.