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2553
Wang S., Li B. Z., Khabsa M., Fang H., Ma H. (2020). Linformer: Self-Attention with Linear Complexity // https://arxiv.org/abs/2006.04768
2554
Zaheer M., Guruganesh G., Dubey A., Ainslie J., Alberti C., Ontanon S., Pham P., Ravula A., Wang Q., Yang L., Ahmed A. (2020). Big Bird: Transformers for Longer Sequences // https://arxiv.org/abs/2007.14062
2555
Choromanski K., Likhosherstov V., Dohan D., Song X., Gane A., Sarlos T., Hawkins P., Davis J., Mohiuddin A., Kaiser L., Belanger D., Colwell L., Weller A. (2020). Rethinking Attention with Performers // https://arxiv.org/abs/2009.14794
2556
Martins P. H., Marinho Z., Martins A. F. T. (2021). ∞-former: Infinite Memory Transformer // https://arxiv.org/abs/2109.00301
2557
Ding J., Ma S., Dong L., Zhang X., Huang S., Wang W., Zheng N., Wei F. (2023). LongNet: Scaling Transformers to 1,000,000,000 Tokens // https://arxiv.org/abs/2307.02486
2558
Tay Y., Bahri D., Yang L., Metzler D., Juan D.-C. (2020). Sparse Sinkhorn Attention // https://arxiv.org/abs/2002.11296
2559
Tay Y., Bahri D., Metzler D., Juan D.-C., Zhao Z., Zheng C. (2020). Synthesizer: Rethinking Self-Attention in Transformer Models // https://arxiv.org/abs/2005.00743
2560
Ma X., Zhou C., Kong X., He J., Gui L., Neubig G., May J., Zettlemoyer L. (2022). Mega: Moving Average Equipped Gated Attention // https://arxiv.org/abs/2209.10655
2561
Yu L., Simig D., Flaherty C., Aghajanyan A., Zettlemoyer L., Lewis M. (2023). MEGABYTE: Predicting Million-byte Sequences with Multiscale Transformers // https://arxiv.org/abs/2305.07185
2562
Tay Y., Dehghani M., Abnar S., Shen Y., Bahri D., Pham P., Rao J., Yang L., Ruder S., Metzler D. (2020). Long Range Arena: A Benchmark for Efficient Transformers // https://arxiv.org/abs/2011.04006
2563
Long-range modeling on LRA (2023) // https://paperswithcode.com/sota/long-range-modeling-on-lra
2564
An C., Gong S., Zhong M., Zhao X., Li M., Zhang J., Kong L., Qiu X. (2023). L-Eval: Instituting Standardized Evaluation for Long Context Language Models // https://arxiv.org/abs/2307.11088
2565
Bai Y., Lv X., Zhang J., Lyu H., Tang J., Huang Z., Du Z., Liu X., Zeng A., Hou L., Dong Y., Tang J., Li J. (2023). LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding // https://arxiv.org/abs/2308.14508
2566
Li Y., Cai T., Zhang Y., Chen D., Dey D. (2022). What Makes Convolutional Models Great on Long Sequence Modeling? // https://arxiv.org/abs/2210.09298
2567
Poli M., Massaroli S., Nguyen E., Fu D. Y., Dao T., Baccus S., Bengio Y., Ermon S., Ré C. (2023). Hyena Hierarchy: Towards Larger Convolutional Language Models // https://arxiv.org/abs/2302.10866
2568
Brown T. B., Mann B., Ryder N., Subbiah M., Kaplan J., Dhariwal P., Neelakantan A., Shyam P., Sastry G., Askell A., Agarwal S., Herbert-Voss A., Krueger G., Henighan T., Child R., Ramesh A., Ziegler D. M., Wu J., Winter C., Hesse C., Chen M., Sigler E., Litwin M., Gray S., Chess B., Clark J., Berner C., McCandlish S., Radford A., Sutskever I., Amodei D. (2020). Language Models are Few-Shot Learners // https://arxiv.org/abs/2005.14165
2569
Karpathy A. (2020) / Twitter // https://twitter.com/karpathy/status/1273788774422441984
2570
Branwen G. (2020). GPT-3 Creative Fiction // https://www.gwern.net/GPT-3
2571
Reynolds L., McDonell K. (2021). Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm // https://arxiv.org/abs/2102.07350
2572
Rebuffi S.-A., Bilen H., Vedaldi A. (2017). Learning multiple visual domains with residual adapters // https://arxiv.org/abs/1705.08045
2573
Houlsby N., Giurgiu A., Jastrzebski S., Morrone B., de Laroussilhe Q., Gesmundo A., Attariyan M., Gelly S. (2019). Parameter-Efficient Transfer Learning for NLP // https://arxiv.org/abs/1902.00751
2574
Hu E. J., Shen Y., Wallis P., Allen-Zhu Z., Li Y., Wang S., Wang L., Chen W. (2021). LoRA: Low-Rank Adaptation of Large Language Models // https://arxiv.org/abs/2106.09685
2575
Xu R., Luo F., Zhang Z., Tan C., Chang B., Huang S., Huang F. (2021). Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning // https://arxiv.org/abs/2109.05687
2576
Duan Z., Zhang H., Wang C., Wang Z., Chen B., Zhou M. (2021). EnsLM: Ensemble Language Model for Data Diversity by Semantic Clustering / Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 2954—2967 // https://doi.org/10.18653/v1/2021.acl-long.230
2577
Conneau A., Kruszewski G., Lample G., Barrault L., Baroni M. (2018). What you can cram into a single vector: Probing sentence embeddings for linguistic properties // https://arxiv.org/abs/1805.01070
2578
Şahin G. G., Vania C., Kuznetsov I., Gurevych I. (2019). LINSPECTOR: Multilingual Probing Tasks for Word Representations // https://arxiv.org/abs/1903.09442
2579
Kim N., Patel R., Poliak A., Wang A., Xia P., McCoy R. T., Tenney I., Ross A., Linzen T., Durme B. V., Bowman S. R., Pavlick E. (2019). Probing What Different NLP Tasks Teach Machines about Function Word Comprehension // https://arxiv.org/abs/1904.11544
2580
Shi X., Padhi I., Knight K. (2016). Does String-Based Neural MT Learn Source Syntax? / Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1526—1534 // https://doi.org/10.18653/v1/D16-1159
2581
Lee J., Tang R., Lin J. (2019). What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning // https://arxiv.org/abs/1911.03090
2582
Li X. L., Liang P. (2021). Prefix-Tuning: Optimizing Continuous Prompts for Generation // https://arxiv.org/abs/2101.00190