Explore and analyze the Top Large Language Model (LLM) security solutions with features. Pick the best LLM security tool of your choice to fit your enterprise requirements perfectly: However, they also introduce significant risks, particularly around data security. Employees may inadvertently use levera...
Join the world’s largest applied NLP community at theNLP Summit 2022from October 4-6, 2022 to learn more about the top large language models and how they are used to solve business problems. The virtual event features three days of immersive, industry-focused content in over 50 technical se...
To address these questions, the team used the most advanced technique currently available: prompt engineering with large language models. This technique allows the model to understand and accurately answer legal questions. The results of the ALQAC 2024 competition demonstrated the effectiveness of the ...
Taught by Prof. Chris Manning at Stanford,CS224n: Deep learning for NLPis a must-take course for anyone interested in natural language processing. From traditional NLP and linguistics concepts all the way up to large language models and ethical challenges, this course provides a comprehensive and...
As part of the OWASP AI Project, a community of experts published a list of the top 10 vulnerabilities seen in Large Language Model (LLM) applications.
指令服从能力需要进一步提升以及物体幻视问题普遍存在等。更多综述细节和榜单详情，可以戳论文查看~多模态大模型榜单：https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation 论文地址：[1]综述：https://arxiv.org/abs/2306.13549[2]评测：https://arxiv.org/abs/2306.13394 ...
7、Are Emergent Abilities of Large Language Models a Mirage?大型语言模型的涌现能力是海市蜃楼吗？涌现能力之所以吸引人，有两个方面：它们的敏锐性，似乎是瞬间从不存在到现在的转变，以及它们的不可预测性，以看似不可预见的模型规模出现。在这里，我们对涌现能力提出了另一种解释：对于特定的任务和模型族，当...
Large Language Models Encode Clinical Knowledge标题: 大型语言模型编码临床知识标签: Deepmind; Google作者: Karan Singhal,Shekoofeh Azizi,Tao Tu,S. Sara Mahdavi,Jason Wei,Hyung Won Chung,Nathan Scales,Ajay Tanwani,Heather Cole-Lewis,Stephen Pfohl,Perry Payne,Martin Seneviratne,Paul Gamble,Chris Kelly,...
多模态大语言模型(Multimodal Large Language Model,MLLM)依赖于LLM丰富的知识储备以及强大的推理和泛化能力来解决多模态问题,目前已经涌现出一些令人惊叹的能力,比如看图写作和看图写代码。但仅根据这些样例很难充分反映MLLM的性能,目前仍然缺乏对MLLM的全面评测。
Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized \textit{prompts} to leverage the in-context learning capabilities of LLMs for recommendation purposes. More...