Researchers
LLMs Can Now Reason in Parallel: UC Berkeley and UCSF Researchers Introduce Adaptive Parallel Reasoning to Scale Inference Efficiently Without Exceeding Context Windows
[ad_1] Large language models (LLMs) have made significant strides in reasoning capabilities, exemplified by breakthrough systems like OpenAI o1 and DeepSeekR1, which utilize test-time compute for search and reinforcement learning to optimize performance. Despite this progress, current methodologies face critical challenges that impede their effectiveness. Serialized chain-of-thought approaches generate excessively long output sequences, increasing latency…
AI companions unsafe for teens under 18, researchers say
[ad_1] As the popularity of artificial intelligence companions surges amongst teens, critics point to warning signs that the risks of use are not worth the potential benefits. Now, in-depth testing of three well-known platforms — Character.AI, Nomi, and Replika — has led researchers at Common Sense Media to an unequivocal conclusion: AI social companions are…
Researchers teach LLMs to solve complex planning challenges
[ad_1] Imagine a coffee company trying to optimize its supply chain. The company sources beans from three suppliers, roasts them at two facilities into either dark or light coffee, and then ships the roasted coffee to three retail locations. The suppliers have different fixed capacity, and roasting costs and shipping costs vary from place to…
NVIDIA AI Researchers Introduce FFN Fusion: A Novel Optimization Technique that Demonstrates How Sequential Computation in Large Language Models LLMs can be Effectively Parallelized
[ad_1] 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…
Researchers from FutureHouse and ScienceMachine Introduce BixBench: A Benchmark Designed to Evaluate AI Agents on Real-World Bioinformatics Task
[ad_1] Modern bioinformatics research is characterized by the constant emergence of complex data sources and analytical challenges. Researchers routinely confront tasks that require the synthesis of diverse datasets, the execution of iterative analyses, and the interpretation of subtle biological signals. High-throughput sequencing, multi-dimensional imaging, and other advanced data collection techniques contribute to an environment where…
Google DeepMind researchers introduce new benchmark to improve LLM factuality, reduce hallucinations
[ad_1] Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Hallucinations, or factually inaccurate responses, continue to plague large language models (LLMs). Models falter particularly when they are given more complex tasks and when users are looking for specific and highly detailed responses. It’s a…
Can AI Models Scale Knowledge Storage Efficiently? Meta Researchers Advance Memory Layer Capabilities at Scale
[ad_1] The field of neural network architectures has witnessed rapid advancements as researchers explore innovative ways to enhance computational efficiency while maintaining or improving model performance. Traditional dense networks rely heavily on computationally expensive matrix operations to encode and store information. This reliance poses challenges when scaling these models for real-world applications that demand extensive…
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