January 1, 2023Open Access

Understanding In-Context Learning via Supportive Pretraining Data

Puntos clave

Los puntos clave no están disponibles para este artículo en este momento.

Resumen

Xiaochuang Han, Daniel Simig, Todor Mihaylov, Yulia Tsvetkov, Asli Celikyilmaz, Tianlu Wang. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo

Cite This Study

Han et al. (Sun,) studied this question.

synapsesocial.com/papers/6a08b5e4ad370a6b44de498a https://doi.org/https://doi.org/10.18653/v1/2023.acl-long.708

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1LSDSem 2017 Shared Task: The Story Cloze Test2017 · 138 citations
2MAUVE: Measuring the Gap Between Neural Text and Human Text using\n Divergence Frontiers2021 · 91 citations
3The Curious Case of Neural Text Degeneration2019 · 1,113 citations
4What Can Transformers Learn In-Context? A Case Study of Simple Function Classes2022 · 59 citations
5Larger language models do in-context learning differently2023 · 100 citations

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo