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Don't Trash Your Old Tech: You Can Recycle Your Phone and Large Appliances Free
You’ve just gotten a shiny new phone — now what do you do with the old one? It might be tempting to shove your e-waste into a corner or toss it into a junk drawer but eventually it all starts to pile up. Before long, you’ll have to figure out where to dispose of your outdated…
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
Large language models (LLMs) have become vital across domains, enabling high-performance applications such as natural language generation, scientific research, and conversational agents. Underneath these advancements lies the transformer architecture, where alternating layers of attention mechanisms and feed-forward networks (FFNs) sequentially process tokenized input. However, with an increase in size and complexity, the computational burden required…
FunSearch: Making new discoveries in mathematical sciences using Large Language Models
Research Published 14 December 2023 Authors Alhussein Fawzi and Bernardino Romera Paredes By searching for “functions” written in computer code, FunSearch made the first discoveries in open problems in mathematical sciences using LLMs Update: In December 2024, we published a report on arXiv showing how our method can be used to amplify human performance in…
Tufa Labs Introduced LADDER: A Recursive Learning Framework Enabling Large Language Models to Self-Improve without Human Intervention
Large Language Models (LLMs) benefit significantly from reinforcement learning techniques, which enable iterative improvements by learning from rewards. However, training these models efficiently remains challenging, as they often require extensive datasets and human supervision to enhance their capabilities. Developing methods that allow LLMs to self-improve autonomously without additional human input or large-scale architectural modifications has…
Like human brains, large language models reason about diverse data in a general way
While early language models could only process text, contemporary large language models now perform highly diverse tasks on different types of data. For instance, LLMs can understand many languages, generate computer code, solve math problems, or answer questions about images and audio. MIT researchers probed the inner workings of LLMs to better understand how they…
This AI Paper from Menlo Research Introduces AlphaMaze: A Two-Stage Training Framework for Enhancing Spatial Reasoning in Large Language Models
Artificial intelligence continues to advance in natural language processing but still faces challenges in spatial reasoning tasks. Visual-spatial reasoning is fundamental for robotics, autonomous navigation, and interactive problem-solving applications. AI systems must effectively interpret structured environments and execute sequential decisions to function in these domains. While traditional maze-solving algorithms, such as depth-first search and A*,…
This AI Paper from UC Berkeley Introduces a Data-Efficient Approach to Long Chain-of-Thought Reasoning for Large Language Models
Large language models (LLMs) process extensive datasets to generate coherent outputs, focusing on refining chain-of-thought (CoT) reasoning. This methodology enables models to break down intricate problems into sequential steps, closely emulating human-like logical reasoning. Generating structured reasoning responses has been a major challenge, often requiring extensive computational resources and large-scale datasets to achieve optimal performance….
This AI Paper Explores Long Chain-of-Thought Reasoning: Enhancing Large Language Models with Reinforcement Learning and Supervised Fine-Tuning
Large language models (LLMs) have demonstrated proficiency in solving complex problems across mathematics, scientific research, and software engineering. Chain-of-thought (CoT) prompting is pivotal in guiding models through intermediate reasoning steps before reaching conclusions. Reinforcement learning (RL) is another essential component that enables structured reasoning, allowing models to recognize and correct errors efficiently. Despite these advancements,…
Bridging Reasoning and Action: The Synergy of Large Concept Models (LCMs) and Large Action Models (LAMs) in Agentic Systems
The advent of advanced AI models has led to innovations in how machines process information, interact with humans, and execute tasks in real-world settings. Two emerging pioneering approaches are large concept models (LCMs) and large action models (LAMs). While both extend the foundational capabilities of large language models (LLMs), their objectives and applications diverge. LCMs…
Ghostbuster: Detecting Text Ghostwritten by Large Language Models
The structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text. Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual…
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