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Outsmarting Uncertainty: How ‘K-Level Reasoning’ from Microsoft Research is Setting New Standards for LLMs

by | Feb 12, 2024 | Uncategorized

Delving into the intricacies of artificial intelligence, particularly within the dynamic reasoning domain, uncovers the pivotal role of Large Language Models (LLMs) in navigating environments that are not just complex but ever-changing. While effective in predictable...

LLMWare Launches SLIMs: Small Specialized Function-Calling Models for Multi-Step Automation

by | Feb 12, 2024 | Uncategorized

As enterprises look to deploy LLMs in more complex production use cases beyond simple knowledge assistants, there is a growing recognition of three interconnected needs: Agents – complex workflows involve multiple steps and require the orchestration of multiple LLM...

Nomic AI Introduces Nomic Embed: Text Embedding Model with an 8192 Context-Length that Outperforms OpenAI Ada-002 and Text-Embedding-3-Small on both Short and Long Context Tasks

by | Feb 12, 2024 | Uncategorized

Nomic AI released an embedding model with a multi-stage training pipeline, Nomic Embed, an open-source, auditable, and high-performing text embedding model. It also has an extended context length supporting tasks such as retrieval-augmented-generation (RAG) and...

Can Large Language Models be Trusted for Evaluation? Meet SCALEEVAL: An Agent-Debate-Assisted Meta-Evaluation Framework that Leverages the Capabilities of Multiple Communicative LLM Agents

by | Feb 12, 2024 | Uncategorized

Despite the utility of large language models (LLMs) across various tasks and scenarios, researchers need help to evaluate LLMs properly in different situations. They use LLMs to check their responses, but a solution must be found. This method is limited because there...

Pinterest Researchers Present an Effective Scalable Algorithm to Improve Diffusion Models Using Reinforcement Learning (RL)

by | Feb 12, 2024 | Uncategorized

Diffusion models are a set of generative models that work by adding noise to the training data and then learn to recover the same by reversing the noising process. This process allows these models to achieve state-of-the-art image quality, making them one of the most...

Meet Graph-Mamba: A Novel Graph Model that Leverages State Space Models SSM for Efficient Data-Dependent Context Selection

by | Feb 12, 2024 | Uncategorized

Graph Transformers need help with scalability in graph sequence modeling due to high computational costs, and existing attention sparsification methods fail to adequately address data-dependent contexts. State space models (SSMs) like Mamba are effective and efficient...

Outsmarting Uncertainty: How ‘K-Level Reasoning’ from Microsoft Research is Setting New Standards for LLMs

LLMWare Launches SLIMs: Small Specialized Function-Calling Models for Multi-Step Automation

Nomic AI Introduces Nomic Embed: Text Embedding Model with an 8192 Context-Length that Outperforms OpenAI Ada-002 and Text-Embedding-3-Small on both Short and Long Context Tasks

Can Large Language Models be Trusted for Evaluation? Meet SCALEEVAL: An Agent-Debate-Assisted Meta-Evaluation Framework that Leverages the Capabilities of Multiple Communicative LLM Agents

Pinterest Researchers Present an Effective Scalable Algorithm to Improve Diffusion Models Using Reinforcement Learning (RL)

Meet Graph-Mamba: A Novel Graph Model that Leverages State Space Models SSM for Efficient Data-Dependent Context Selection

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