Preference learning is closely aligned with judgment and evaluation tasks, especially comparative and ranking judgment. In addition to works that directly adopt or augment preference learning datasets for supervised finetuning judge LLMs, several studies apply preference learning techniques to enhance LLMs...
Methods: A scoping review was conducted to explore the challenges and barriers of using LLMs, in diagnostic medicine with a focus on digital pathology. A comprehensive search was conducted using electronic databases, including PubMed and Google Scholar, for relevant articles published within the past...
163 -- 1:37:40 App LLM for Verilog RTL Generation-贺培鑫博士 92 -- 1:30:47 App Large Models & Logical Verification-周海教授&李由博士 114 -- 1:26:13 App Knowledge-Enhanced LLMs with Retrieval-Augmented Generation-徐海琳 299 -- 57:49 App Large Language Model in EDA-余备副教授 12...
In machine learning (ML) applications, assets include not only the ML models themselves, but also the datasets, algorithms, and deployment tools that are essential in the development, training, and implementation of these models. Efficient management of ML assets is critical to ensure optimal resour...
So long as appropriate steps are taken to overcome the challenges and risks of using of LLMs for LCA, the opportunities presented by integrating the generative AI models can streamline the LCA process and result in significant benefits for the LCA practitioner.Journal of Cleaner ProductionNathan ...
innovation and economic growth—but it will take time to get there because fostering community-driven development and addressing critical ethical and privacy concerns require careful planning, collaboration, and iterative refinements. Businesses already are using open-source LLMs for a number of ...
As a result, the goals of model editing and knowledge unlearning differ, with the former requiring a more robust learning approach. model edit要求更严格。emmm,其实unlearning和model edit里面的 knowledge erase是一样的,或者说是一个子集罢了。 方法 现有的方法分为 参数优化 参数合并 上下文学习 三类。
更快更轻量级的大语言模型:当前挑战与未来发展方向综述 摘要 尽管大语言模型(LLMs)表现出色,但由于推理过程中需要大量的计算资源和内存,其广泛应用面临挑战。最近在模型压缩和系统级优化方法方面的进展旨在提高大语言模型的推理效率。本综述概述了这些方法,重点介绍了
Large language models (LLMs) are sophisticated AI systems designed to process and generate human-like text using natural language processing. They are trained using vast amounts of text data from various sources, allowing them to mimic human-like text generation and demonstrate advanced capabilities ...
27 Nov 2024·Omkar Khade,Shruti Jagdale,Abhishek Phaltankar,Gauri Takalikar,Raviraj Joshi· Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, yet challenges persist in adapting these models for low-resource languages. In this study, we investigate the effects of Low...