Voxel51的动态

Check out the new paper, "KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches." This research provides an in-depth analysis of various KV cache compression strategies, evaluating their effectiveness in handling long context inputs for large language models (LLMs). The paper offers valuable insights into the trade-offs between compression efficiency and model performance, making it a crucial resource for developers and researchers in AI and machine learning. The findings highlight significant improvements in memory usage and inference speed without compromising accuracy. For those interested in the technical aspects and practical applications of KV cache compression, this paper is essential reading. It offers comprehensive benchmarks and solutions that contribute meaningfully to advancements in the field. Read the full paper here: [https://lnkd.in/gDYzgNEc] #AI #MachineLearning #LLMs #KVCacheCompression #DataScience #Research #Innovation #DeepLearning

?? Breaking the Code: AI Language Insights Ever wondered how artificial intelligence truly "understands" language? Join Core CEO Ravi Ganesan as he unravels the fascinating world of transformer architectures and machine learning, revealing how AI transforms complex word patterns into meaningful communication. Discover the magic behind large language models in this must-watch exploration! Watch our full webinar on demand for an in-depth exploration of using AI in behavioral health—available, here: https://hubs.ly/Q02Z_PqM0 #LargeLanguageModels #AI #BehavioralHealth

Ever wondered if intelligent agents could team up to push the boundaries of science? Our latest preprint takes a stab at this fascinating question! In this study, we explore the potential of a multi-agent system, composed of Large Language Models, to replicate the methodology of a high-impact scientific publication. While our AI teammates showed promise in dissecting complex research and executing statistical analyses, they also faced their fair share of challenges—from the unpredictability of code generation to the intricacies of data procurement. Dive into our findings to discover the nuances of employing AI in scientific research. Can these agents truly automate the process, or are they just scratching the surface? Read our initial analysis and let's discuss the evolving role of AI in the realm of science. https://lnkd.in/gcV9iCcH #AI #MachineLearning #GenerativeAI #Innovation #DataScience #LLM

One bold claim from a recent article we wrote is that neglecting certain evaluation methodologies can directly lead to failures in large language models. This raises an important discussion around the importance of robust assessment frameworks in machine learning. How do we ensure our models perform reliably and ethically? I encourage everyone in our community to reflect on these methodologies and their impact on project outcomes. Do you think embracing these evaluation techniques is essential for future developments in AI, or do you have a different perspective? Let’s share our thoughts and insights. #MachineLearning #DataScience #AI #LLM #Collaboration https://lnkd.in/gEnkb8a9

?? Excited to share an intriguing paper I came across: "xLSTM: Extended Long Short-Term Memory." This research explores an alternative approach to Large Language Models (LLMs) that could address their notorious resource hunger. ?? While the formulas might be over my head, I thoroughly enjoyed the read and appreciated the innovative perspective. Dipping my toes into the latest AI science inspires me! Curious minds, check it out here: https://lnkd.in/dgqHgyZW #AI #MachineLearning #LLM #Innovation

A blog in my monthly series on science for people with no background in the subject ... but remain curious. This one is about the science underlying AI. https://lnkd.in/eaHDkWMb

As the world continues to evolve into the Fourth Industrial Revolution (4IR), the University of Johannesburg (UJ) is at the forefront of critical discussions on how artificial intelligence (AI) is reshaping our societies. During the course of this year, our UJ Cloudebates? focus on the theme "Impact through innovation in an AI world", exploring how AI technologies, especially large language model (LLM) generative AI, can drive positive societal change while preserving our humanity. Join the conversation and gain valuable insights into the future of AI. Watch the debates now at https://lnkd.in/esmKDFz #UJAllTheWay #4IR #UJCloudebates

Thrilled to announce that I have completed the "LLM Observability: Evaluations" course by Arize AI! This program has provided me with valuable strategies for evaluating large language models (LLMs), such as performance metrics, qualitative assessment, bias and fairness, robustness, and user-centric evaluation. #AI #MachineLearning #LLM #AIethics

Exploring the Capabilities and Limitations of Large Language Models (#LLMs) in #SocialScience Sharing an insightful presentation by Luuk Schmitz, highlighting what Large Language Models (LLMs) can and can’t do in the realm of social science research, outlining use cases, validation, and model selection: https://lnkd.in/dNGB67hM #AI #AIrevolution #LLMs #SocialScience

?? Perplexity is one of the most popular heuristic LLM evaluation methods, but what is it, really? ?? Perplexity seeks to quantify the “uncertainty” a model experiences when predicting the next token in a sequence. Quantifying uncertainty in language models helps us judge when it might need human oversight or further training, allowing us to handle those cases differently. ?? My new article explores perplexity’s mathematical basis, underlying intuitions, and limitations, as well as provides a full-code tutorial using #Opik, an LLM evaluation platform by Comet. ?? Check it out here: https://lnkd.in/eNPeknte ?? #AI #GenerativeAI