​[[{“value”:” In Neural Networks, understanding how to optimize performance with a given computational budget is crucial. More processing power devoted to training neural networks usually results in better performance. However, choosing between expanding the training dataset and raising the model’s parameters is crucial when scaling… Read More »Data Complexity and Scaling Laws in Neural Language Models Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Large language models (LLMs) have proven their potential to handle multiple tasks and perform extremely well across various applications. However, it is challenging for LLMs to generate accurate information, especially when the knowledge is less represented in their training data. To overcome this challenge,… Read More »Nearest Neighbor Speculative Decoding (NEST): An Inference-Time Revision Method for Language Models to Enhance Factuality and Attribution Using Nearest-Neighbor Speculative Decoding Sajjad Ansari Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The development and application of large language models (LLMs) have experienced significant advancements in Artificial Intelligence (AI). These models have demonstrated exceptional capabilities in understanding and generating human language, impacting various areas such as natural language processing, machine translation, and automated content creation. As… Read More »Ant Group Proposes MetRag: A Multi-Layered Thoughts Enhanced Retrieval Augmented Generation Framework Nikhil Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Scale AI has announced the launch of SEAL Leaderboards, an innovative and expert-driven ranking system for large language models (LLMs). This initiative is a product of the Safety, Evaluations, and Alignment Lab (SEAL) at Scale, which is dedicated to providing neutral, trustworthy evaluations of… Read More »Scale AI’s SEAL Research Lab Launches Expert-Evaluated and Trustworthy LLM Leaderboards Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” LLMs possess extraordinary natural language understanding capabilities, primarily derived from pretraining on extensive textual data. However, their adaptation to new or domain-specific knowledge is limited and can lead to inaccuracies. Knowledge Graphs (KGs) offer structured data storage, aiding in updates and facilitating tasks like… Read More »GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style Mohammad Asjad Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Natural Language Processing (NLP) has seen transformative advancements over the past few years, largely driven by the developing of sophisticated language models like transformers. Among these advancements, Retrieval-Augmented Generation (RAG) stands out as a cutting-edge technique that significantly enhances the capabilities of language models.… Read More »How RAG helps Transformers to build customizable Large Language Models: A Comprehensive Guide Aswin Ak Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Retrieval-augmented generation (RAG) is a potent strategy that improves the capabilities of Large Language Models (LLMs) by integrating outside knowledge.  However, RAG is prone to a particular type of attack known as retrieval corruption. In these types of attacks, malicious actors introduce destructive sections… Read More »RobustRAG: A Unique Defense Framework Developed for Opposing Retrieval Corruption Attacks in Retrieval-Augmented Generation (RAG) Systems Tanya Malhotra Artificial Intelligence Category – MarkTechPost

​[[{“value”:” K2 is a cutting-edge large language model (LLM) developed by LLM360 in collaboration with MBZUAI and Petuum. This model, known as K2-65B, boasts 65 billion parameters and is fully reproducible, meaning all artifacts, including code, data, model checkpoints, and intermediate results, are open-sourced and… Read More »LLM360 Introduces K2: A Fully-Reproducible Open-Sourced Large Language Model Efficiently Surpassing Llama 2 70B with 35% Less Computational Power Asif Razzaq Artificial Intelligence Category – MarkTechPost

​[[{“value”:” Multimodal machine learning is a cutting-edge research field combining various data types, such as text, images, and audio, to create more comprehensive and accurate models. By integrating these different modalities, researchers aim to enhance the model’s ability to understand and reason about complex tasks.… Read More »Matryoshka Multimodal Models With Adaptive Visual Tokenization: Enhancing Efficiency and Flexibility in Multimodal Machine Learning Sana Hassan Artificial Intelligence Category – MarkTechPost

​[[{“value”:” The advent of deep neural networks (DNNs) has led to remarkable improvements in controlling artificial agents using the optimization of reinforcement learning or evolutionary algorithms. However, most neural networks show structural rigidity, binding their architectures to specific input and output space. This inflexibility is… Read More »Structurally Flexible Neural Networks: An AI Approach to Solve a Symmetric Dilemma for Optimizing Units and Shared Parameters Sajjad Ansari Artificial Intelligence Category – MarkTechPost