This survey provides a comprehensive overview of the current state of research on in-context learning (ICL) for natural language processing. It begins by formally defining ICL and clarifying its relationship to related concepts like prompt learning and few-shot learning.
The paper then delves into the various techniques used to enhance ICL capabilities, including pretraining strategies, prompt design, and related analysis. Pretraining methods like reorganizing pretraining corpora and meta-distillation can boost the ICL abilities of large language models. Prompt design involves strategies for selecting, reformatting, and ordering demonstration examples, as well as incorporating task instructions.
The survey also explores the factors that influence ICL performance, both in the pretraining and inference stages. Pretraining factors include the diversity and distribution of the training data, as well as model architecture and scale. Inference-stage factors include the input-label mapping, the diversity and similarity of demonstration examples, and the order of the examples.
To explain the underlying mechanisms of ICL, the paper reviews research on the functional modules within Transformers, such as attention heads and computational layers, that contribute to ICL capabilities. It also discusses theoretical interpretations of ICL from Bayesian, gradient descent, and other perspectives.
Finally, the survey examines various application scenarios for ICL, including data engineering, model augmentation, and knowledge updating. It also highlights the key challenges facing ICL, such as efficiency, scalability, and generalization, and suggests potential directions for future research.
Overall, this comprehensive survey provides a valuable resource for understanding the current state of ICL research and identifying promising avenues for further exploration.
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by Qingxiu Dong... klo arxiv.org 09-30-2024
https://arxiv.org/pdf/2301.00234.pdfSyvällisempiä Kysymyksiä