Fetching the paper…
Reading the bibliography…
Recent advances in large language models (LLMs) have transformed software development by automatically generating code from natural language.
A literature review of the anchoring effect
Furnham, A. and Boo, H. C · 2010
Earlier work this paper cites.
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
Earlier work this paper cites.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
An analysis of encoder representations in transformer-based machine translation
Raganato, A. and Tiedemann, J · 2018
Earlier work this paper cites.
Boolq: Exploring the surprising difficulty of natural yes/no questions, 2019
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
Earlier work this paper cites.
Domain differential adaptation for neural machine translation
Dou, Z.-Y., Wang, X., Hu, J., and Neubig, G · 2019
Earlier work this paper cites.
Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned, 2019
Voita, E., Talbot, D., Moiseev, F., Sennrich, R., and Titov, I · 2019
Earlier work this paper cites.
Attention in natural language processing
Galassi, A., Lippi, M., and Torroni, P · 2020
Earlier work this paper cites.
Scaling laws for neural language models, 2020
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D · 2020
Earlier work this paper cites.
Program synthesis with large language models, 2021
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., and Sutton, C · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Paino, A., Tezak, N., Tang, J., Babuschkin, I., Balaji, S., Jain, S., Saunders, W., Hesse, C., Carr, A. N., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems, 2021
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
Earlier work this paper cites.
Dexperts: Decoding-time controlled text generation with experts and anti-experts, 2021
Liu, A., Sap, M., Lu, X., Swayamdipta, S., Bhagavatula, C., Smith, N. A., and Choi, Y · 2021
Earlier work this paper cites.
GPT-J-6B: A 6 billion parameter autoregressive language model, 2021
Wang, B. and Komatsuzaki, A · 2021
Earlier work this paper cites.
Natgen: generative pre-training by “naturalizing” source code
Chakraborty, S., Ahmed, T., Ding, Y., Devanbu, P. T., and Ray, B · 2022
Earlier work this paper cites.
Overcoming a theoretical limitation of self-attention, 2022
Chiang, D. and Cholak, P · 2022
Earlier work this paper cites.
Llm.int8(): 8-bit matrix multiplication for transformers at scale, 2022
Dettmers, T., Lewis, M., Belkada, Y., and Zettlemoyer, L · 2022
Earlier work this paper cites.
Truthfulqa: Measuring how models mimic human falsehoods, 2022
Lin, S., Hilton, J., and Evans, O · 2022
Earlier work this paper cites.
Coherence boosting: When your pretrained language model is not paying enough attention
Malkin, N., Wang, Z., and Jojic, N · 2022
Earlier work this paper cites.
Spt-code: Sequence-to-sequence pre-training for learning source code representations
Niu, C., Li, C., Ng, V., Ge, J., Huang, L., and Luo, B · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback, 2022
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P., Leike, J., and Lowe, R · 2022
Cited alongside, same era.
Challenging big-bench tasks and whether chain-of-thought can solve them, 2022
Suzgun, M., Scales, N., Schärli, N., Gehrmann, S., Tay, Y., Chung, H. W., Chowdhery, A., Le, Q. V., Chi, E. H., Zhou, D., and Wei, J · 2022
Cited alongside, same era.
Finetuned language models are zero-shot learners, 2022
Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., and Le, Q. V · 2022
Cited alongside, same era.
What does transformer learn about source code?, 2022
Zhang, K., Li, G., and Jin, Z · 2022
Cited alongside, same era.
Codegrag: Extracting composed syntax graphs for retrieval augmented cross-lingual code generation, 2024
Du, K., Rui, R., Chai, H., Fu, L., Xia, W., Wang, Y., Tang, R., Yu, Y., and Zhang, W · 2024
Closest in time.
Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024
Guo, D., Zhu, Q., Yang, D., Xie, Z., Dong, K., Zhang, W., Chen, G., Bi, X., Wu, Y., Li, Y. K., Luo, F., Xiong, Y., and Liang, W · 2024
Closest in time.
How good are low-bit quantized llama3 models? an empirical study, 2024
Huang, W., Ma, X., Qin, H., Zheng, X., Lv, C., Chen, H., Luo, J., Qi, X., Liu, X., and Magno, M · 2024
Closest in time.
Livecodebench: Holistic and contamination free evaluation of large language models for code, 2024
Jain, N., Han, K., Gu, A., Li, W.-D., Yan, F., Zhang, T., Wang, S., Solar-Lezama, A., Sen, K., and Stoica, I · 2024
Closest in time.
Self-planning code generation with large language models
Jiang, X., Dong, Y., Wang, L., Fang, Z., Shang, Q., Li, G., Jin, Z., and Jiao, W · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gptq: Accurate post-training quantization for generative pre-trained transformers, 2023
Frantar, E., Ashkboos, S., Hoefler, T., and Alistarh, D · 2023
Cited alongside, same era.
The benefits of bad advice: Autocontrastive decoding across model layers
Gera, A., Friedman, R., Arviv, O., Gunasekara, C., Sznajder, B., Slonim, N., and Shnarch, E · 2023
Cited alongside, same era.
Mitigating object hallucinations in large vision-language models through visual contrastive decoding, 2023
Leng, S., Zhang, H., Chen, G., Li, X., Lu, S., Miao, C., and Bing, L · 2023
Cited alongside, same era.
Starcoder: may the source be with you!
Li, R., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., Akiki, C., Jia, L., Chim, J., Liu, Q., et al · 2023
Cited alongside, same era.
Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation, 2023
Liu, J., Xia, C. S., Wang, Y., and Zhang, L · 2023
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis, 2023
Nijkamp, E., Pang, B., Hayashi, H., Tu, L., Wang, H., Zhou, Y., Savarese, S., and Xiong, C · 2023
Cited alongside, same era.
An empirical study of model errors and user error discovery and repair strategies in natural language database queries
Ning, Z., Zhang, Z., Sun, T., Tian, Y., Zhang, T., and Li, T. J.-J · 2023
Cited alongside, same era.
Do large language models pay similar attention like human programmers when generating code?
Kou, B., Chen, S., Wang, Z., Ma, L., and Zhang, T · 2024
Closest in time.
Codechain: Towards modular code generation through chain of self-revisions with representative sub-modules, 2024
Le, H., Chen, H., Saha, A., Gokul, A., Sahoo, D., and Joty, S · 2024
Closest in time.
Evaluating quantized large language models, 2024
Li, S., Ning, X., Wang, L., Liu, T., Shi, X., Yan, S., Dai, G., Yang, H., and Wang, Y · 2024
Closest in time.
Starcoder 2 and the stack v2: The next generation, 2024
Lozhkov, A., Li, R., Allal, L. B., Cassano, F., Lamy-Poirier, J., Tazi, N., Tang, A., Pykhtar, D., Liu, J., Wei, Y., Liu, T., Tian, M., Kocetkov, D., Zucker, A., Belkada, Y., Wang, Z., Liu, Q., Abulkhanov, D., Paul, I., Li, Z., Li, W.-D., Risdal, M., Li, J., Zhu, J., Zhuo, T. Y., Zheltonozhskii, E., Dade, N. O. O., Yu, W., Krauß, L., Jain, N., Su, Y., He, X., Dey, M., Abati, E., Chai, Y., Muennighoff, N., Tang, X., Oblokulov, M., Akiki, C., Marone, M., Mou, C., Mishra, M., Gu, A., Hui, B., Dao, T., Zebaze, A., Dehaene, O., Patry, N., Xu, C., McAuley, J., Hu, H., Scholak, T., Paquet, S., Robinson, J., Anderson, C. J., Chapados, N., Patwary, M., Tajbakhsh, N., Jernite, Y., Ferrandis, C. M., Zhang, L., Hughes, S., Wolf, T., Guha, A., von Werra, L., and de Vries, H · 2024
Closest in time.
Insights into natural language database query errors: From attention misalignment to user handling strategies
Ning, Z., Tian, Y., Zhang, Z., Zhang, T., and Li, T. J.-J · 2024
Closest in time.
Code llama: Open foundation models for code, 2024
Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., Rapin, J., Kozhevnikov, A., Evtimov, I., Bitton, J., Bhatt, M., Ferrer, C. C., Grattafiori, A., Xiong, W., Défossez, A., Copet, J., Azhar, F., Touvron, H., Martin, L., Usunier, N., Scialom, T., and Synnaeve, G · 2024
Closest in time.
Mitigating hallucinations and off-target machine translation with source-contrastive and language-contrastive decoding, 2024
Sennrich, R., Vamvas, J., and Mohammadshahi, A · 2024
Closest in time.
Sqlucid: Grounding natural language database queries with interactive explanations
Tian, Y., Kummerfeld, J. K., Li, T. J.-J., and Zhang, T · 2024
Closest in time.
Where do large language models fail when generating code?, 2024
Wang, Z., Zhou, Z., Song, D., Huang, Y., Chen, S., Ma, L., and Zhang, T · 2024
Closest in time.
Efficient streaming language models with attention sinks, 2024
Xiao, G., Tian, Y., Chen, B., Han, S., and Lewis, M · 2024
Closest in time.
Tell your model where to attend: Post-hoc attention steering for llms, 2024
Zhang, Q., Singh, C., Liu, L., Liu, X., Yu, B., Gao, J., and Zhao, T · 2024
Closest in time.
Weak-to-strong jailbreaking on large language models, 2024
Zhao, X., Yang, X., Pang, T., Du, C., Li, L., Wang, Y.-X., and Wang, W. Y · 2024
Closest in time.
Zhuo, T. Y., Vu, M. C., Chim, J., Hu, H., Yu, W., Widyasari, R., Yusuf, I. N. B., Zhan, H., He, J., Paul, I., Brunner, S., Gong, C., Hoang, T., Zebaze, A. R., Hong, X., Li, W.-D., Kaddour, J., Xu, M., Zhang, Z., Yadav, P., Jain, N., Gu, A., Cheng, Z., Liu, J., Liu, Q., Wang, Z., Lo, D., Hui, B., Muennighoff, N., Fried, D., Du, X., de Vries, H., and Werra, L. V · 2024
Closest in time.
Enhancing code generation via bidirectional comment-level mutual grounding, 2025
Di, Y. and Zhang, T · 2025
Closest in time.
Text-to-sql domain adaptation via human-llm collaborative data annotation
Tian, Y., Lee, D., Wu, F., Mai, T., Qian, K., Sahai, S., Zhang, T., and Li, Y · 2025
Closest in time.