[CL]《How to Train Data-Efficient LLMs》N Sachdeva, B Coleman, W Kang, J Ni, L Hong, E H. Chi, J Caverlee, J McAuley, D Z Cheng [Google DeepMind] (2024) O网页链接 #机器学习##人工智能##论文# û收藏 50 2
本文发现,当学习新信息时,LLMs表现出“启动”效应:学习新知识可能导致模型在不相关的情况下不恰当的使用该知识。本文是对这一现象的深入研究和解决方案探索。 研究内容: 启动效应: 启动效应指的是LLM倾向于在不属于它的上下文中不恰当地应用新学到的信息。例如,如果一个模型了解到“在Blandgive,香蕉的主要颜色是朱...
Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely 摘要 外部数据增强的大语言模型 (LLM) 在完成真实世界任务方面表现出令人印象深刻的能力。外部数据不仅增强了模型的领域专业知识和时间相关性,而且减少了幻觉的发生率,从而提高了输出...
Hugging Face also providestransformers, a Python library that streamlines running a LLM locally. The following example uses the library to run an older GPT-2microsoft/DialoGPT-mediummodel. On the first run, the Transformers will download the model, and you can have five interactions with it. Th...
Sensitive or critical decisions: Do not use LLMs to automate tasks requiring high accuracy or involving sensitive data. For example, legal or medical recommendations demand human expertise to avoid errors with major consequences. High-stakes creative work: When originality and creativity are paramount,...
Learn to create diverse test cases using both intrinsic and extrinsic metrics and balance the performance with resource management for reliable LLMs.
We show that large language models (LLMs), such as ChatGPT, can guide the robot design process, on both the conceptual and technical level, and we propose new human–AI co-design strategies and their societal implications.
Data professionals must learn more about what LLMs can do and how to keep the models honest. In the meantime, LLMs will be able to assist analysts, but not replace them. Organizations need analysts to craft prompts carefully andverify the accuracyof all outputs -- that is, until developers...
InstructLab is a community-driven project designed to simplify the process of contributing to and enhancing large language models (LLMs) through synthetic data generation.
Discover the family of LLMs available and the elements to consider when evaluating which LLM is the best for your use case.