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Large Language Model

The AI research document from the University of California, Berkeley, introduces ArCHer: an innovative machine learning platform beneficial for enhancing progressive decision-making in expansive language models.

The technology industry has been heavily focused on the development and enhancement of machine decision-making capabilities, especially with large language models (LLMs). Traditionally, decision-making in machines was improved through reinforcement learning (RL), a process of learning from trial and error to make optimal decisions in different environments. However, the conventional RL methodologies tend to concentrate…

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IBM AI Research Unveils API-BLEND: A Comprehensive Resource for Training and Rigorous Assessment of Tool-Enhanced LLMs.

The implementation of APIs into Large Language Models (LLMs) is a major step towards complex, functional AI systems like hotel reservations or job applications through conversational interfaces. However, the development of these systems relies heavily on the LLM's ability to accurately identify APIs, fill the necessary parameters, and sequence API calls based on the user's…

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EasyQuant: Transforming Big Language Model Quantization through Tencent’s Algorithm that doesn’t require Data

The constant progression of natural language processing (NLP) has brought about an era of advanced, large language models (LLMs) that can accomplish complex tasks with a considerably high level of accuracy. However, these models are costly in terms of computational requirements and memory, limiting their application in environments with finite resources. Model quantization is a…

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Transforming AI Conversation: A Look at How FUSECHAT Combines Several Language Models to Create a Superior, More Memory-Efficient LLM.

The development of Large Language Models (LLMs) such as GPT and LLaMA has significantly revolutionized natural language processing (NLP). They have found use in a broad range of functions, causing a growing demand for custom LLMs amongst individuals and corporations. However, the development of these LLMs is resource-intensive, posing a significant challenge for potential users. To…

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The Future of Code Generation Championed by StarCoder2 and The Stack v2: Implementing Large Language Models in a Revolutionary Way

The BigCode project has successfully developed StarCoder2, the second iteration of an advanced large language model designed to revolutionise the field of software development. A collaboration between over 30 top universities and institutions, StarCoder2 uses machine learning to optimise code generation, making it easier to fix bugs and automate routine coding tasks. Training StarCoder2 on…

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Optimizing Language Models for Efficiency and Recall: Presenting BASED for Fast, High-Quality Text Production

Language models' performance pertains to their efficiency and ability to recall information, with demand for these capabilities high as artificial intelligence continues to tackle the intricacies of human language. Researchers from Stanford University, Purdue University, and the University at Buffalo have developed an architecture, called Based, differing significantly from traditional methodologies. Its aim is to…

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IBM Research Introduces SimPlan: Narrowing the Divide in AI Planning using Advanced Hybrid Broad Language Model Technology

IBM Research has unveiled "SimPlan", an innovative method designed to enhance the planning capabilities of large language models (LLMs), which traditionally struggle with mapping out action sequences toward achieving an optimal outcome. The SimPlan method, developed by researchers from IBM, combines the linguistic skills of LLMs with the structured approach of classical planning algorithms, addressing…

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Researchers at the University of Southern California have suggested a machine learning framework known as DeLLMa (Decision-making Large Language Model Assistant), created specifically to improve the precision of decision-making processes in environments filled with uncertainty.

In a world filled with complexity and unpredictability, making informed decisions often proves difficult. The conventional strategies and human expertise often fall short, especially in sectors such as business, finance, and agriculture that involve high stakes and uncertainty. Enter DeLLMa – a Decision-making Large Language Model Assistant developed by researchers from the University of Southern…

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Researchers at Microsoft AI have engineered an advanced model named ResLoRA to enhance Low-Rank Adaptation (LoRA).

Researchers from the School of Computer Science and Engineering at Beihang University in Beijing, China, and Microsoft have developed an improved framework for Low-rank Adaptation (LoRA), known as ResLoRA. Improving LoRA is necessary to address the challenge of high costs which are incurred when fine-tuning Large Language Models (LLMs) on specific datasets, due to their…

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Researchers from NVIDIA have unveiled Nemotron-4 15B, a massive multilingual language model with 15 billion parameters, which has been trained on 8 trillion text tokens.

The development of artificial intelligence models that can handle both human language and code has been a significant focus for researchers. The goal is to create models that break down linguistic barriers and facilitate more intuitive interactions between humans and machines. This challenge encompasses understanding multiple languages and the intricate syntax and semantics of programming…

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Introducing PlanGPT: The Pioneering Large-Scale Language Model Framework for Tackling Challenges in Urban Planning and Spatial Development.

The urban and spatial planning sector is a rapidly evolving field that increasingly requires the integration of advanced technology. This not only expedites planning processes, but also improves the precision and efficacy of urban development strategies. Amid this technological revolution, the advent of specialised large language models (LLMs), designed for specific industries, has occurred. This…

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