A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Unknown
Data2vec is a general self-supervised learning framework that uses the same learning method for speech, NLP, and computer vision.
Unknown
Introduces retrieval-augmented generation (RAG), combining pre-trained parametric and non-parametric memory for knowledge-intensive NLP tasks.
Bohan Li, Yutai Hou, Wanxiang Che
This survey categorizes data augmentation methods in NLP into paraphrasing, noising, and sampling based on the diversity of augmented data, and reviews their applications and challenges.
Bonan Min, Hayley Ross, Elior Sulem, et al.
This survey reviews the shift to PLM-driven NLP, covering pre-training, fine-tuning, prompting, and text generation, and discusses limitations and future directions.
Andrew Walker, Jerik Leung, Aishwarya Alagappan, et al.
This study uses NLP and LLMs to analyze Reddit lupus narratives, extracting multidimensional biopsychosocial pain insights to support patient-centered rheumatology care.
Shubham Vatsal, Harsh Dubey
A survey of 44 papers on 39 prompt engineering methods across 29 NLP tasks, showing how structured prompts improve LLM performance without retraining.
Ronan Collobert, Jason Weston, Léon Bottou, et al.
A unified neural network architecture that learns internal representations from unlabelled data achieves state-of-the-art on multiple NLP tasks without task-specific feature engineering.
Unknown
A human-curated instruction-following dataset spanning 65 languages to bridge the language gap in NLP.
Unknown
BloombergGPT is a 50B parameter language model trained on a mix of general and financial data, achieving top performance on financial NLP tasks without sacrificing general capabilities.
Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.
Instruction tuning on 62 NLP datasets with natural language templates enables a 137B parameter model to achieve strong zero-shot performance on unseen tasks.
Hangbo Bao, Li Dong, Furu Wei, et al.
UniLMv2 introduces Pseudo-Masked Language Modeling (PMLM) to unify autoencoding and partially autoregressive objectives, improving performance across diverse NLP tasks.
Zhilin Yang, Zihang Dai, Yiming Yang, et al.
XLNet combines autoregressive and autoencoding pretraining via permutation language modeling, outperforming BERT on NLP tasks.