Fetching the paper…
Reading the bibliography…
Ever since the development of GPT-3 in the natural language processing (NLP) field, in-context learning (ICL) has played an essential role in utilizing large language models (LLMs).
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language resources and evaluation , vol. 42, no. 4, pp. 335–359, 2008
2008
Earlier work this paper cites.
S. Arik, J. Chen, K. Peng, W. Ping, and Y. Zhou, “Neural voice cloning with a few samples,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
Y. Wang, D. Stanton, Y. Zhang, R.-S. Ryan, E. Battenberg, J. Shor, Y. Xiao, Y. Jia, F. Ren, and R. A. Saurous, “Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis,” in International conference on machine learning . PMLR, 2018, pp. 5180–5189
2018
Earlier work this paper cites.
P. Warden, “Speech commands: A dataset for limited-vocabulary speech recognition,” 2018
2018
Earlier work this paper cites.
S. Castro, D. Hazarika, V. Pérez-Rosas, R. Zimmermann, R. Mihalcea, and S. Poria, “Towards multimodal sarcasm detection (an _obviously_ perfect paper),” in Proceedings of the 57th Conference of ACL , 2019, pp. 4619–4629
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
L. T. Benamer and O. A. Alkishriwo, “Database for arabic speech commands recognition,” in CEST , 2020
2020
Earlier work this paper cites.
A. Kolesau and D. Šešok, “Unsupervised pre-training for voice activation,” Applied Sciences , vol. 10, no. 23, p. 8643, 2020
2020
Earlier work this paper cites.
Y.-Y. Tsai, P.-Y. Chen, and T.-Y. Ho, “Transfer learning without knowing: Reprogramming black-box machine learning models with scarce data and limited resources,” in International Conference on Machine Learning . PMLR, 2020, pp. 9614–9624
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
A. Polyak, Y. Adi, J. Copet, E. Kharitonov, K. Lakhotia, W. Hsu, A. Mohamed, and E. Dupoux, “Speech resynthesis from discrete disentangled self-supervised representations,” in Interspeech . ISCA, 2021, pp. 3615–3619
2021
Earlier work this paper cites.
K. Lakhotia et al. , “On generative spoken language modeling from raw audio,” Transactions of the Association for Computational Linguistics , vol. 9, pp. 1336–1354, 2021
2021
Earlier work this paper cites.
Y.-Y. Lin, W.-Z. Zheng, W. C. Chu, J.-Y. Han, Y.-H. Hung, G.-M. Ho, C.-Y. Chang, and Y.-H. Lai, “A speech command control-based recognition system for dysarthric patients based on deep learning technology,” Applied Sciences , 2021
2021
Cited alongside, same era.
W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 3451–3460, 2021
2021
Cited alongside, same era.
2022
Cited alongside, same era.
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler et al. , “Emergent abilities of large language models,” Transactions on Machine Learning Research , 2022
2022
2023
Closest in time.
C. Wang, S. Chen, Y. Wu, Z. Zhang, L. Zhou, S. Liu, Z. Chen, Y. Liu, H. Wang, J. Li, L. He, S. Zhao, and F. Wei, “Neural codec language models are zero-shot text to speech synthesizers,” 2023
2023
Closest in time.
2023
Closest in time.
Y. Gu, L. Dong, F. Wei, and M. Huang, “Pre-training to learn in context,” in ACL (1) . Association for Computational Linguistics, 2023, pp. 4849–4870
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
2022
Cited alongside, same era.
S. Min, M. Lewis, L. Zettlemoyer, and H. Hajishirzi, “Metaicl: Learning to learn in context,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2022, pp. 2791–2809
2022
Cited alongside, same era.
M. Chen, J. Du, R. Pasunuru, T. Mihaylov, S. Iyer, V. Stoyanov, and Z. Kozareva, “Improving in-context few-shot learning via self-supervised training,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2022, pp. 3558–3573
2022
Cited alongside, same era.
T. Sun, Y. Shao, H. Qian, X. Huang, and X. Qiu, “Black-box tuning for language-model-as-a-service,” in International Conference on Machine Learning . PMLR, 2022, pp. 20 841–20 855
2022
Cited alongside, same era.
OpenAI, “Introducing ChatGPT,” 2022, https://openai.com/blog/chatgpt
2022
Cited alongside, same era.
E. Kharitonov et al. , “Text-free prosody-aware generative spoken language modeling,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 8666–8681
2022
Cited alongside, same era.
S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer, “Rethinking the role of demonstrations: What makes in-context learning work?” 2022
2022
Cited alongside, same era.
K.-W. Chang, W.-C. Tseng, S.-W. Li, and H. yi Lee, “An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks,” in Proc. Interspeech 2022 , 2022, pp. 5005–5009
2022
Cited alongside, same era.
Z. Borsos et al. , “Audiolm: a language modeling approach to audio generation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
D. Dai, Y. Sun, L. Dong, Y. Hao, S. Ma, Z. Sui, and F. Wei, “Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers,” in ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models , 2023
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Computing Surveys , vol. 55, no. 9, pp. 1–35, 2023
2023
Closest in time.
K.-W. Chang, M.-H. Chen, Y.-P. Lin, J. N. Hsu, P. K.-M. Huang, C.-y. Huang, S.-W. Li, and H.-y. Lee, “Prompting and adapter tuning for self-supervised encoder-decoder speech model,” in 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) . IEEE, 2023, pp. 1–8
2023
Closest in time.
A. Chen, Y. Yao, P.-Y. Chen, Y. Zhang, and S. Liu, “Understanding and improving visual prompting: A label-mapping perspective,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 19 133–19 143
2023
Closest in time.
K.-W. Chang, Y.-K. Wang, H. Shen, I. thing Kang, W.-C. Tseng, S.-W. Li, and H. yi Lee, “SpeechPrompt v2: Prompt tuning for speech classification tasks,” 2023
2023
Closest in time.