class="title">

2699

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

2700

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

2701

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

2702

Dehghani M., Gouws S., Vinyals O., Uszkoreit J., Kaiser Ł. (2018). Universal Transformers // https://arxiv.org/abs/1807.03819

2703

Wang Z., Ma Y., Liu Z., Tang J. (2019). R-Transformer: Recurrent Neural Network Enhanced Transformer // https://arxiv.org/abs/1907.05572

2704

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

2705

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

2706

Graves A. (2016). Adaptive Computation Time for Recurrent Neural Networks // https://arxiv.org/abs/1603.08983

2707

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

2708

Sapunov G. (2019). Adaptive Computation Time (ACT) in Neural Networks // https://moocaholic.medium.com/adaptive-computation-time-act-in-neural-networks-part-1-2a28484b53df

2709

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

2710

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

2711

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

2712

Gu A., Goel K., Ré C. (2021). Efficiently Modeling Long Sequences with Structured State Spaces // Статья: https://arxiv.org/abs/2111.00396

2713

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

2714

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

2715

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

2716

Smith J. T. H., Warrington A., Linderman S. W. (2022). Simplified State Space Layers for Sequence Modeling // https://arxiv.org/abs/2208.04933

2717

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

2718

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

2719

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

2720

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

2721

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

2722

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

2723

OpenAI (2023). ChatGPT plugins // https://openai.com/blog/chatgpt-plugins

2724

* Сегодня для такого синтеза часто используют термин «генерация, дополненная поиском» (Retrieval-augmented Generation, RAG).

2725

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

2726

Heafield K. (2011). KenLM: Faster and Smaller Language Model Queries // https://kheafield.com/papers/avenue/kenlm.pdf

2727

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