​[[{“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

​[[{“value”:” In robotics, understanding the position and movement of a sensor suite within its environment is crucial. Traditional methods, called Simultaneous Localization and Mapping (SLAM), often face challenges with unsynchronized sensor data and require complex computations. These methods must estimate the position at discrete time… Read More »Hyperion: A Novel, Modular, Distributed, High-Performance Optimization Framework Targeting both Discrete and Continuous-Time SLAM Applications Niharika Singh Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large Language Models (LLMs) like GPT-4 exhibit impressive capabilities in text generation tasks such as summarization and question answering. However, they often produce “hallucinations,” generating content that is factually incorrect or contextually irrelevant. The problem is particularly acute when the LLMs are provided with… Read More »Enhancing LLM Reliability: The Lookback Lens Approach to Hallucination Detection Shreya Maji Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The Retrieval-Augmented Generation (RAG) pipeline includes four major steps— generating embeddings for queries and documents, retrieving relevant documents, analyzing the retrieved data, and generating the final response. Each of these steps. requires separate queries and tools, resulting in a cumbersome, time-consuming, and potentially error-prone… Read More »Korvus: An All-in-One Open-Source RAG (Retrieval-Augmented Generation) Pipeline Built for Postgres Pragati Jhunjhunwala Artificial Intelligence Category – MarkTechPost