In this work, we study how well the learned weights of a neural network utilize the space available to them. This notion is related to capacity, but additionally incorporates the interaction of the network architecture with the dataset. Most learned weights appear to be full… Read More »Revealing the Utilized Rank of Subspaces of Learning in Neural Networks Apple Machine Learning Research
We investigate the out-of-domain generalization of random feature (RF) models and Transformers. We first prove that in the ‘generalization on the unseen (GOTU)’ setting, where training data is fully seen in some part of the domain but testing is made on another part, and for… Read More »On the Minimal Degree Bias in Generalization on the Unseen for non-Boolean Functions Apple Machine Learning Research
Large Language Model (LLM) agents, capable of performing a broad range of actions, such as invoking tools and controlling robots, show great potential in tackling real-world challenges. LLM agents are typically prompted to produce actions by generating JSON or text in a pre-defined format, which… Read More »CodeAct: Your LLM Agent Acts Better when Generating Code Apple Machine Learning Research
[[{“value”:” Large Language Models (LLMs) have demonstrated remarkable proficiency in language generation tasks. However, their training process, which involves unsupervised learning from extensive datasets followed by supervised fine-tuning, presents significant challenges. The primary concern stems from the nature of pre-training datasets, such as Common Crawl,… Read More »Samsung Researchers Introduce LoRA-Guard: A Parameter-Efficient Guardrail Adaptation Method that Relies on Knowledge Sharing between LLMs and Guardrail Models Mohammad Asjad Artificial Intelligence Category – MarkTechPost
[[{“value”:” Language model adaptation is a crucial area in artificial intelligence, focusing on enhancing large pre-trained language models to work effectively across various languages. This research is vital for enabling these models to understand and generate text in multiple languages, which is essential for global… Read More »Branch-and-Merge Method: Enhancing Language Adaptation in AI Models by Mitigating Catastrophic Forgetting and Ensuring Retention of Base Language Capabilities while Learning New Languages Nikhil Artificial Intelligence Category – MarkTechPost
[[{“value”:” Large language models (LLMs) have shown exceptional capabilities in understanding and generating human language, making substantial contributions to applications such as conversational AI. Chatbots powered by LLMs can engage in naturalistic dialogues, providing a wide range of services. The effectiveness of these chatbots relies… Read More »Arena Learning: Transforming Post-Training of Large Language Models with AI-Powered Simulated Battles for Enhanced Efficiency and Performance in Natural Language Processing Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Evaluating the performance of large language model (LLM) inference systems using conventional metrics presents significant challenges. Metrics such as Time To First Token (TTFT) and Time Between Tokens (TBT) do not capture the complete user experience during real-time interactions. This gap is critical in… Read More »Metron: A Holistic AI Framework for Evaluating User-Facing Performance in LLM Inference Systems Aswin Ak Artificial Intelligence Category – MarkTechPost
[[{“value”:” Large Language Models (LLMs) built on the Transformer architecture have recently attained important technological milestones. The remarkable skills of these models in comprehending and producing writing that resembles that of a human have had a significant impact on a variety of Artificial Intelligence (AI)… Read More »Optimizing Large Language Models (LLMs) on CPUs: Techniques for Enhanced Inference and Efficiency Tanya Malhotra Artificial Intelligence Category – MarkTechPost
[[{“value”:” Collecting, monitoring, and maintaining a web data pipeline can be daunting and time-consuming when dealing with large amounts of data. Traditional approaches’ struggles can compromise data quality and availability with pagination, dynamic content, bot detection, and site modifications. Building an in-house technical staff or… Read More »Meet Reworkd: An AI Startup that Automates End-to-end Data Extraction Dhanshree Shripad Shenwai Artificial Intelligence Category – MarkTechPost
[[{“value”:” Transformer-based LLMs like ChatGPT and LLaMA excel in tasks requiring domain expertise and complex reasoning due to their large parameter sizes and extensive training data. However, their substantial computational and storage demands limit broader applications. Quantization addresses these challenges by converting 32-bit parameters to… Read More »FBI-LLM (Fully BInarized Large Language Model): An AI Framework Using Autoregressive Distillation for 1-bit Weight Binarization of LLMs from Scratch Sana Hassan Artificial Intelligence Category – MarkTechPost