DistilBERT DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, 2019, Paper ALBERT ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, 2019, Paper UniLM Unified Language Model Pre-training for Natural Language Understanding and Generation, 2019 Pape...
(35)Multi-Method Self-Training: Improving Code Generation With Text, And Vice Versa面对同样的输入,LLM有多种处理方法,本文让这些方法相互增强,可惜是需要微调的,作者甚至只在BLOOM-176B上做了实验表明有用(36)REASONING LIKE ARISTOTLE: DIAGNOSING AND IMPROVING LANGUAGE MODELS FOR LOGICAL REASONING THROUGH ...
[4] Yao Y, Dong Q, Guan J, et al. Cuge: A chinese language understanding and generation evaluation benchmark[J]. arXiv preprint arXiv:2112.13610, 2021. [5] Chiang, Wei-Lin et al. Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality. 2023. [6] Huang, Yuzhen...
其中,大型语言模型(LLMs)如GPT(Generative Pre-trained Transformer)已成为引领NLP潮流的重要力量。GPT的核心思想是通过生成式预训练来提升语言理解能力,这一思想在《Improving Language Understanding by Generative Pre-Training》这篇论文中得到了充分的体现和验证。 一、GPT模型简介 GPT是一种基于Transformer架构的大型语...
Language Generation, Translation, and Comprehension 作者： Lewis et al. @facebook Paper:https://arxiv .org/abs/1910.13461 用于自然语言生成、翻译和理解的去噪S2S预训练。如前所述，BERT 类型的编码器风格的 LLM 通常更适合预测建模任务，而 GPT 类型的解码器风格的 LLM 更擅长生成文本。为了两全其美，...
22个大型语言模型，从Bert到GPT-4 大型语言模型（LLM）是人工智能领域机器学习技术在自然语言处理（NLP）方向上的产物，通常包含一个参数众多的神经元网络，使用大量的文本数据进行训练。这些模型已经开始展露出完成多种语言任务的能力，包括理解、编写、推理和对话等，并且有可能超越语言范围成为更多人工智能工具的有力的...
UCB CS 194/294-267 Understanding Large Language Models: Foundations and Safety 斯坦福大学《CS 236 2023 fall Deep Generative Models|深度生成模型》(18课全)GPT4翻译-中英字幕 GPT中英字幕课程资源 4615 0 (超爽中英!) 2024公认最好的【吴恩达机器学习】教程!附课件代码 Machine Learning Specialization 吴恩达...
[12]Yao Y, Dong Q, Guan J, et al. Cuge: A chinese language understanding and generation evaluation benchmark[J]. arXiv preprint arXiv:2112.13610, 2021. 链接:http://cuge.baai.ac.cn[13]Guo B, Zhang X, Wang Z, et al. How close is chatgpt to human experts? comparison corpus, eva...
Natural language understanding and generation tasks using LLMs, e.g., storytelling, marketing copywriting, etc. LLMs for health care, protein synthesis, etc. Challenges Ethics, social economics, and trustworthiness of LLMs. Data labeling and quality issues for training LLMs. ...
LLMs have an interconnected link in language understanding and generation using Natural Language Processing (NLP) and Natural Language Generation (NLG). NLP enables computers to understand and interpret human language, while NLG generates human-readable text from structured data. LLMs, such as Gene...