A direct correlation exists between an LLM’s training corpus quality and its capabilities. Consequently, researchers have invested a great deal […]
Category: Machine Learning
Advancing Parallel Programming with HPC-INSTRUCT: Optimizing Code LLMs for High-Performance Computing
LLMs have revolutionized software development by automating coding tasks and bridging the natural language and programming gap. While highly effective […]
This AI Paper Proposes TALE: An AI Framework that Reduces Token Redundancy in Chain-of-Thought (CoT) Reasoning by Incorporating Token Budget Awareness
Large Language Models (LLMs) have shown significant potential in reasoning tasks, using methods like Chain-of-Thought (CoT) to break down complex […]
Researchers from Tsinghua University Propose ReMoE: A Fully Differentiable MoE Architecture with ReLU Routing
The development of Transformer models has significantly advanced artificial intelligence, delivering remarkable performance across diverse tasks. However, these advancements often […]
aiXplain Introduces a Multi-AI Agent Autonomous Framework for Optimizing Agentic AI Systems Across Diverse Industries and Applications
Agentic AI systems have revolutionized industries by enabling complex workflows through specialized agents working in collaboration. These systems streamline operations, […]
Hypernetwork Fields: Efficient Gradient-Driven Training for Scalable Neural Network Optimization
Hypernetworks have gained attention for their ability to efficiently adapt large models or train generative models of neural representations. Despite […]
Collective Monte Carlo Tree Search (CoMCTS): A New Learning-to-Reason Method for Multimodal Large Language Models
In today’s world, Multimodal large language models (MLLMs) are advanced systems that process and understand multiple input forms, such as […]
YuLan-Mini: A 2.42B Parameter Open Data-efficient Language Model with Long-Context Capabilities and Advanced Training Techniques
Large language models (LLMs) built using transformer architectures heavily depend on pre-training with large-scale data to predict sequential tokens. This […]
Unveiling Privacy Risks in Machine Unlearning: Reconstruction Attacks on Deleted Data
Machine unlearning is driven by the need for data autonomy, allowing individuals to request the removal of their data’s influence […]
Meet SemiKong: The World’s First Open-Source Semiconductor-Focused LLM
The semiconductor industry enables advancements in consumer electronics, automotive systems, and cutting-edge computing technologies. The production of semiconductors involves sophisticated […]