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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….

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 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…

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…

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…

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…

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…

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…

In AI image generation, text-to-image diffusion models have become a focal point due to their ability to create photorealistic images from textual descriptions. These models use complex algorithms to interpret text and translate it into visual content, simulating creativity and understanding previously thought unique to humans. This technology holds immense potential across various domains, from…

Recent strides in language models (LMs)and tool usage have given rise to semi-autonomous agents like WebGPT, AutoGPT, and ChatGPT plugins that operate in real-world scenarios. While these agents hold promise for enhanced LM capabilities, transitioning from text interactions to real-world actions through tools brings forth unprecedented risks. Failures to follow instructions could lead to financial…