Meet Orion-14B: A New Open-source Multilingual Large Language Model Trained on 2.5T Tokens Including Chinese, English, Japanese, and Korean

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With the advancement of AI in recent times, large language models are being used in many fields. These models are trained on larger datasets and require bigger training datasets. These are used in various natural language processing (NLP) tasks, such as dialogue systems, machine translation, information retrieval, etc. There has been thorough research in LLMs…

Researchers from the Tokyo Institute of Technology Introduce ProtHyena: A Fast and Efficient Foundation Protein Language Model at Single Amino Acid Resolution

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Proteins are essential for various cellular functions, providing vital amino acids for humans. Understanding proteins is crucial for human biology and health, requiring advanced machine-learning models for protein representation. Self-supervised pre-training, inspired by natural language processing, has significantly improved protein sequence representation. However, existing models need help handling longer sequences and maintaining contextual understanding. Strategies…

This AI Paper from Sun Yat-sen University and Tencent AI Lab Introduces FUSELLM: Pioneering the Fusion of Diverse Large Language Models for Enhanced Capabilities

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The development of large language models (LLMs) like GPT and LLaMA has marked a significant milestone. These models have become indispensable tools for various natural language processing tasks. However, creating these models from scratch involves considerable costs, immense computational resources, and substantial energy consumption. This has led to an increasing interest in developing cost-effective alternatives….

Google DeepMind Researchers Propose WARM: A Novel Approach to Tackle Reward Hacking in Large Language Models Using Weight-Averaged Reward Models

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In recent times, Large Language Models (LLMs) have gained popularity for their ability to respond to user queries in a more human-like manner, accomplished through reinforcement learning. However, aligning these LLMs with human preferences in reinforcement learning from human feedback (RLHF) can lead to a phenomenon known as reward hacking. This occurs when LLMs exploit…

Tensoic AI Releases Kan-Llama: A 7B Llama-2 LoRA PreTrained and FineTuned on ‘Kannada’ Tokens

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Tensoic has recently introduced Kannada Llama (Kan-LLaMA) to address the limitations of language models (LLMs), focusing specifically on proprietary characteristics, computational resources, and barriers to broader research community contributions. Emphasize the importance of open models using mouth to facilitate innovation in natural language processing (NLP) and machine translation with emphasis. Despite the success of models…

Meet Medusa: An Efficient Machine Learning Framework for Accelerating Large Language Models (LLMs) Inference with Multiple Decoding Heads

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The most recent advancement in the field of Artificial Intelligence (AI), i.e., Large Language Models (LLMs), has demonstrated some great improvement in language production. With model sizes reaching billions of parameters, these models are stepping into every domain, ranging from healthcare and finance to education. Though these models have shown amazing capabilities, the development of…

This Report from Microsoft AI Reveals the Impact of Fine-Tuning and Retrieval-Augmented Generation RAG on Large Language Models in Agriculture

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Great strides have been made in Artificial Intelligence, especially in Large Language Models like GPT-4 and Llama 2. These models, driven by advanced deep learning techniques and vast data resources, have demonstrated remarkable performance across various domains. Their potential in diverse sectors such as agriculture, healthcare, and finance is immense, as they assist in complex…

This AI Paper Proposes COPlanner: A Machine Learning-based Plug-and-Play Framework that can be Applied to any Dyna-Style Model-based Methods

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One of the critical challenges in model-based reinforcement learning (MBRL) is managing imperfect dynamics models. This limitation of MBRL becomes particularly evident in complex environments, where the ability to forecast accurate models is crucial yet difficult, often leading to suboptimal policy learning. The challenge is achieving accurate predictions and ensuring these models can adapt and…

Revolutionizing Fluid Dynamics: Integrating Physics-Informed Neural Networks with Tomo-BOS for Advanced Flow Analysis

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Background Oriented Schlieren (BOS) imaging is an effective technique for visualizing and quantifying fluid flow. BOS is cost-effective and flexible, unlike other methods like Particle Image Velocimetry (PIV) and Laser-Induced Fluorescence (LIF). It relies on the distortion of objects in a density-varying medium due to light refraction, with digital image correlation or optical flow algorithms…

Meet RAGxplorer: An interactive AI Tool to Support the Building of Retrieval Augmented Generation (RAG) Applications by Visualizing Document Chunks and the Queries in the Embedding Space

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Understanding how well they comprehend and organize information is crucial in advanced language models. A common challenge arises in visualizing the intricate relationships between different document parts, especially when using complex models like the Retriever-Answer Generator (RAG). Existing tools can only sometimes provide a clear picture of how chunks of information relate to each other…