Охота на электроовец. Большая книга искусственного интеллекта - Сергей Сергеевич Марков
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Wei J., Wang X., Schuurmans D., Bosma M., Ichter B., Xia F., Chi E., Le Q., Zhou D. (2022). Chain-of-Thought Prompting Elicits Reasoning in Large Language Models // https://arxiv.org/abs/2201.11903
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Yao S., Yu D., Zhao J., Shafran I., Griffiths T. L., Cao Y., Narasimhan K. (2023). Tree of Thoughts: Deliberate Problem Solving with Large Language Models // https://arxiv.org/abs/2305.10601
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Besta M., Blach N., Kubicek A., Gerstenberger R., Gianinazzi L., Gajda J., Lehmann T., Podstawski M., Niewiadomski H., Nyczyk P., Hoefler T. (2023). Graph of Thoughts: Solving Elaborate Problems with Large Language Models // https://arxiv.org/abs/2308.09687
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Dehghani M., Gouws S., Vinyals O., Uszkoreit J., Kaiser Ł. (2018). Universal Transformers // https://arxiv.org/abs/1807.03819
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Wang Z., Ma Y., Liu Z., Tang J. (2019). R-Transformer: Recurrent Neural Network Enhanced Transformer // https://arxiv.org/abs/1907.05572
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Dai Z., Yang Z., Yang Y., Carbonell J., Le Q. V., Salakhutdinov R. (2019). Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context // https://arxiv.org/abs/1901.02860
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Giannou A., Rajput S., Sohn J.-Y., Lee K., Lee J. D., Papailiopoulos D. (2023). Looped Transformers as Programmable Computers // https://arxiv.org/abs/2301.13196
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Graves A. (2016). Adaptive Computation Time for Recurrent Neural Networks // https://arxiv.org/abs/1603.08983
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Fojo D., Campos V., Giro-i-Nieto X. (2018). Comparing Fixed and Adaptive Computation Time for Recurrent Neural Networks // https://arxiv.org/abs/1803.08165
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Sapunov G. (2019). Adaptive Computation Time (ACT) in Neural Networks // https://moocaholic.medium.com/adaptive-computation-time-act-in-neural-networks-part-1-2a28484b53df
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Orvieto A., Smith S. L., Gu A., Fernando A., Gulcehre C., Pascanu R., De S. (2023). Resurrecting Recurrent Neural Networks for Long Sequences // https://arxiv.org/abs/2303.06349
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Peng B., Alcaide E., Anthony Q., Albalak A., Arcadinho S., Cao H., Cheng X., Chung M., Grella M., GV K. K., He X., Hou H., Kazienko P., Kocon J., Kong J., Koptyra B., Lau H., Mantri K. S. I., Mom F., Saito A., Tang X., Wang B., Wind J. S., Wozniak S., Zhang R., Zhang Z., Zhao Q., Zhou P., Zhu J., Zhu R. (2023). Reinventing RNNs for the Transformer Era // https://arxiv.org/abs/2305.13048
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Fu D. Y., Dao T., Saab K. K., Thomas A. W., Rudra A., Ré C. (2022). Hungry Hungry Hippos: Towards Language Modeling with State Space Models // https://arxiv.org/abs/2212.14052
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Gu A., Goel K., Ré C. (2021). Efficiently Modeling Long Sequences with Structured State Spaces // Статья: https://arxiv.org/abs/2111.00396
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Gu A., Johnson I., Timalsina A., Rudra A., Ré C. (2022). How to Train Your HiPPO: State Space Models with Generalized Orthogonal Basis Projections // https://arxiv.org/abs/2206.12037
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Hasani R., Lechner M., Wang T.-H., Chahine M., Amini A., Rus D. (2022). Liquid Structural State-Space Models // https://arxiv.org/abs/2209.12951
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Gu A., Gupta A., Goel K., Ré C. (2022). On the Parameterization and Initialization of Diagonal State Space Models // https://arxiv.org/abs/2206.11893
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Smith J. T. H., Warrington A., Linderman S. W. (2022). Simplified State Space Layers for Sequence Modeling // https://arxiv.org/abs/2208.04933
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Sun Y., Dong L., Huang S., Ma S., Xia Y., Xue J., Wang J., Wei F. (2023). Retentive Network: A Successor to Transformer for Large Language Models // https://arxiv.org/abs/2307.08621
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Thoppilan R., Freitas D. D., Hall J., Shazeer N., Kulshreshtha A., Cheng H., Jin A., Bos T., Baker L., Du Y., Li Y., Lee H., Zheng H. S., Ghafouri A., Menegali M., Huang Y., Krikun M., Lepikhin D., Qin J., Chen D., Xu Y., Chen Z., Roberts A., Bosma M., Zhao V., Zhou Y., Chang C., Krivokon I., Rusch W., Pickett M., Srinivasan P., Man L., Meier-Hellstern K., Morris M. R., Doshi T., Santos R. D., Duke T., Soraker J., Zevenbergen B., Prabhakaran V., Diaz M., Hutchinson B., Olson K., Molina A., Hoffman-John E., Lee J., Aroyo L., Rajakumar R., Butryna A., Lamm M., Kuzmina V., Fenton J., Cohen A., Bernstein R., Kurzweil R., Aguera-Arcas B., Cui C., Croak M., Chi E., Le Q. (2022). LaMDA: Language Models for Dialog Applications // https://arxiv.org/abs/2201.08239
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Schick T., Dwivedi-Yu J., Dessì R., Raileanu R., Lomeli M., Zettlemoyer L., Cancedda N., Scialom T. (2023). Toolformer: Language Models Can Teach Themselves to Use Tools // https://arxiv.org/abs/2302.04761
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Hao S., Liu T., Wang Z., Hu Z. (2023). ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings // https://arxiv.org/abs/2305.11554
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Shen Y., Song K., Tan X., Li D., Lu W., Zhuang Y. (2023). HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face // https://arxiv.org/abs/2303.17580
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Patil S. G., Zhang T., Wang X., Gonzalez J. E. (2023). Gorilla: Large Language Model Connected with Massive APIs // https://arxiv.org/abs/2305.15334
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OpenAI (2023). ChatGPT plugins // https://openai.com/blog/chatgpt-plugins
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* Сегодня для такого синтеза часто используют термин «генерация, дополненная поиском» (Retrieval-augmented Generation, RAG).
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Schlag I., Sukhbaatar S., Celikyilmaz A., Yih W.-t., Weston J., Schmidhuber J., Li X. (2023). Large Language Model Programs // https://arxiv.org/abs/2305.05364
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Heafield K. (2011). KenLM: Faster and Smaller Language Model Queries // https://kheafield.com/papers/avenue/kenlm.pdf
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Borgeaud S., Mensch A., Hoffmann J., Cai T., Rutherford E., Millican K., van den Driessche G., Lespiau J.-B., Damoc B., Clark A., de Las Casas D., Guy A., Menick J., Ring R., Hennigan T., Huang S., Maggiore L., Jones C., Cassirer A., Brock A., Paganini
