Titlebook: Representation Learning for Natural Language Processing; Zhiyuan Liu,Yankai Lin,Maosong Sun Book‘‘‘‘‘‘‘‘ 2023Latest edition The Editor(s)

Coeval

原來(lái)

樹(shù)上結(jié)蜜糖

Robust Representation Learning,ies different robustness needs and characterizes important robustness problems in NLP representation learning, including backdoor robustness, adversarial robustness, out-of-distribution robustness, and interpretability. We also discuss current solutions and future directions for each problem.

stress-response

Biomedical Knowledge Representation Learning,cal research. In this chapter, with biomedical knowledge as the core, we launch a discussion on knowledge representation and acquisition as well as biomedical knowledge-guided NLP tasks and explain them in detail with practical scenarios. We also discuss current research progress and several future directions.

松果

Pre-trained Models for Representation Learning,ng, from pre-training tasks to adaptation approaches for specific tasks. After that, we discuss several advanced topics toward better pre-trained representations, including better model architecture, multilingual, multi-task, efficient representations, and chain-of-thought reasoning.

UTTER

OpenBMB: Big Model Systems for Large-Scale Representation Learning, computation and expertise of big model applications. In this chapter, we will introduce the core toolkits in OpenBMB, including BMTrain for efficient training, OpenPrompt and OpenDelta for efficient tuning, BMCook for efficient compression, and BMInf for efficient inference.

wall-stress

忘川河

清醒

鬼魂