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2677
Rafailov R., Sharma A., Mitchell E., Ermon S., Manning C. D., Finn C. (2023). Direct Preference Optimization: Your Language Model is Secretly a Reward Model // https://arxiv.org/abs/2305.18290
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Bai Y., Kadavath S., Kundu S., Askell A., Kernion J., Jones A., Chen A., Goldie A., Mirhoseini A., McKinnon C., Chen C., Olsson C., Olah C., Hernandez D., Drain D., Ganguli D., Li D., Tran-Johnson E., Perez E., Kerr J., Mueller J., Ladish J., Landau J., Ndousse K., Lukosuite K., Lovitt L., Sellitto M., Elhage N., Schiefer N., Mercado N., DasSarma N., Lasenby R., Larson R., Ringer S., Johnston S., Kravec S., Showk S. E., Fort S., Lanham T., Telleen-Lawton T., Conerly T., Henighan T., Hume T., Bowman S. R., Hatfield-Dodds Z., Mann B., Amodei D., Joseph N., McCandlish S., Brown T., Kaplan J. (2022). Constitutional AI: Harmlessness from AI Feedback // https://arxiv.org/abs/2212.08073
2679
Аверкиев С. (2023). Это не чат, это GigaChat. Русскоязычная ChatGPT от Сбера. / Хабр, 24 апр 2023 // https://habr.com/ru/companies/sberbank/articles/730108/
2680
Bommasani R., Hudson D. A, Adeli E., Altman R., Arora S., von Arx S., Bernstein M. S., Bohg J., Bosselut A., Brunskill E., Brynjolfsson E., Buch S., Card D., Castellon R., Chatterji N., Chen A., Creel K., David J. Q., Demszky D., Donahue C., Doumbouya M., Durmus E., Ermon S., Etchemendy J., Ethayarajh K., Fei-Fei L., Finn C., Gale T., Gillespie L., Goel K., Goodman N., Grossman S., Guha N., Hashimoto T., Henderson P., Hewitt J., Ho D. E., Hong J., Hsu K., Huang J., Icard T., Jain S., Jurafsky D., Kalluri P., Karamcheti S., Keeling G., Khani F., Khattab O., Koh P. W., Krass M., Krishna R., Kuditipudi R., Kumar A., Ladhak F., Lee M., Lee T., Leskovec J., Levent I., Li X. L., Li X., Ma T., Malik A., Manning C. D., Mirchandani S., Mitchell E., Munyikwa Z., Nair S., Narayan A., Narayanan D., Newman B., Nie A., Niebles J. C., Nilforoshan H., Nyarko J., Ogut G., Orr L., Papadimitriou I., Park J. S., Piech C., Portelance E., Potts C., Raghunathan A., Reich R., Ren H., Rong F., Roohani Y., Ruiz C., Ryan J., Ré C., Sadigh D., Sagawa S., Santhanam K., Shih A., Srinivasan K., Tamkin A., Taori R., Thomas A. W., Tramèr F., Wang R. E., Wang W., Wu B., Wu J., Wu Y., Xie S. M., Yasunaga M., You J., Zaharia M., Zhang M., Zhang T., Zhang X., Zhang Y. (2021). On the Opportunities and Risks of Foundation Models // https://arxiv.org/abs/2108.07258
2681
Dao T., Fu D. Y., Ermon S., Rudra A., Ré C. (2022). FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness // https://arxiv.org/abs/2205.14135
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Dao T. (2023). FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning // https://arxiv.org/abs/2307.08691
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Shang Y., Yuan Z., Wu Q., Dong Z. (2023). PB-LLM: Partially Binarized Large Language Models // https://arxiv.org/abs/2310.00034
2684
Nagel M., Fournarakis M., Amjad R. A., Bondarenko Y., van Baalen M., Blankevoort T. (2021). A White Paper on Neural Network Quantization // https://arxiv.org/abs/2106.08295
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Gholami A., Kim S., Dong Z., Yao Z., Mahoney M. W., Keutzer K. (2021). A Survey of Quantization Methods for Efficient Neural Network Inference // https://arxiv.org/abs/2103.13630
2686
Dettmers T., Pagnoni A., Holtzman A., Zettlemoyer L. (2023). QLoRA: Efficient Finetuning of Quantized LLMs // https://arxiv.org/abs/2305.14314
2687
Rush A. (2023). llama2.rs // https://github.com/srush/llama2.rs
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Li X., Yao Y., Jiang X., Fang X., Meng X., Fan S., Han P., Li J., Du L., Qin B., Zhang Z., Sun A., Wang Y. (2023). FLM-101B: An Open LLM and How to Train It with $100K Budget // https://arxiv.org/abs/2309.03852
2689
Bengio Y., Louradour J., Collobert R., Weston J. (2009). Curriculum Learning / ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 41–48. // https://doi.org/10.1145/1553374.1553380
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Graves A., Bellemare M. G., Menick J., Munos R., Kavukcuoglu K. (2017). Automated Curriculum Learning for Neural Networks // https://arxiv.org/abs/1704.03003
2691
Li C., Zhang M., He Y. (2022). The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models // https://openreview.net/forum?id=JpZ5du_Kdh
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Li S. (2023). Variable Sequence Length Training for Long-Context Large Language Models / Large Language Model, NLP, Deep Learning, Machine Learning, Blog, Developer Blog, July 22, 2023. // https://www.cerebras.net/blog/variable-sequence-length-training-for-long-context-large-language-models/
2693
DeepSpeed Data Efficiency: A composable library that makes better use of data, increases training efficiency, and improves model quality (2023). / deepspeed.ai, September 26, 2023. // https://www.deepspeed.ai/tutorials/data-efficiency/
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Fernandez J., Downey D. (2018). Sampling Informative Training Data for RNN Language Models / Proceedings of ACL 2018, Student Research Workshop, pp. 9–13. // https://doi.org/10.18653/v1/P18-3002
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Wang H., Huang M., Huang R., Hong L., Xu H., Hu T., Liang X., Li Z. (2023). Boosting Visual-Language Models by Exploiting Hard Samples // https://arxiv.org/abs/2305.05208
2696
Keles F. D., Hegde C. (2023). On The Computational Complexity of Self-Attention. / Proceedings of Machine Learning Research, Vol. 201, pp. 1–23, 2023 // https://proceedings.mlr.press/v201/duman-keles23a/duman-keles23a.pdf
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* Серебряная пуля — метафора, означающая простое решение сложной проблемы.
2698
Tay Y., Dehghani M., Abnar S., Chung H. W., Fedus W., Rao J., Narang S., Tran V. Q., Yogatama D., Metzler D. (2022). Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? // https://arxiv.org/abs/2207.10551