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Large Language Model

Huawei’s AI research unveils DenseSSM, an innovative machine learning methodology designed to optimize the transfer of concealed data amongst various levels in State Space Models (SSMs).

The field of large language models (LLMs) has witnessed significant advances thanks to the introduction of State Space Models (SSMs). Offering a lower computational footprint, SSMs are seen as a welcome alternative. The recent development of DenseSSM represents a significant milestone in this regard. Designed by a team of researchers at Huawei's Noah's Ark Lab,…

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This Chinese AI report presents ShortGPT: A Fresh AI Method for Trimming Extensive Language Models (LLMs) rooted in Layer Redundancy.

The rapid development in Large Language Models (LLMs) has seen billion- or trillion-parameter models achieve impressive performance across multiple fields. However, their sheer scale poses real issues for deployment due to severe hardware requirements. The focus of current research has been on scaling models to improve performance, following established scaling laws. This, however, emphasizes the…

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Improving the Security of Large Language Models (LLM) to Protect Against Threats from Fine-Tuning: A Strategy Using Enhanced Backdoor Alignment

Large Language Models (LLMs) such as GPT-4 and Llama-2, while highly capable, require fine-tuning with specific data tailored to various business requirements. This process can expose the models to safety threats, most notably the Fine-tuning based Jailbreak Attack (FJAttack). The introduction of even a small number of harmful examples during the fine-tuning phase can drastically…

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Transforming LLM Training through GaLore: A Novel Machine Learning Method to Boost Memory Efficiency while Maintaining Excellent Performance.

The challenges associated with training large language models (LLMs) given their memory-intensive nature can be significant. Traditional methods for reducing memory consumption frequently involve compressing model weights, commonly leading to a decrease in model performance. A new approach being called Gradient Low-Rank Projection (GaLore) is now being proposed by researchers from various institutions, including the…

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Interpreting the Genetic Code of Extensive Language Models: An In-depth Review on Data Sets, Hurdles, and Prospective Paths

Large Language Models (LLMs) play a crucial role in the rapidly advancing field of artificial intelligence, particularly in natural language processing. The quality, diversity, and scope of LLMs are directly linked to their training datasets. As the complexity of human language and the demands on LLMs to mirror this complexity increase, researchers are developing new…

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Microsoft AI Research unveils Orca-Math, a small language model (SLM) consisting of 7 billion parameters. This model has been finely-tuned from the Mistral 7B model.

The field of educational technology continues to evolve, yielding enhancements in teaching methods and learning experiences. Mathematics, in particular, tends to be challenging, requiring tailored solutions to cater to the diverse needs of students. The focus currently lies in developing effective and scalable tools for teaching and assessing mathematical problem-solving skills across a wide spectrum…

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Meta AI introduces ‘Wukong’: An Innovative Machine Learning Framework with Efficient Dense Scaling Characteristics for Large-Scale Recommendation’s Scaling Law.

In the field of machine learning applications, recommendation systems are critical to help customize user experiences on digital platforms, such as e-commerce and social media. However, traditional recommendation models struggle to manage the complexity and size of contemporary datasets. As a solution to this, Wukong, a product of Meta Platforms, Inc., introduces a unique architecture…

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Are LLMs capable of debugging programs similarly to human programmers? Researchers from UCSD present LDB: A Debugging Framework founded on machine learning that utilizes LLMs.

Researchers from the University of California, San Diego, have pioneered a ground-breaking method of debugging code in software development using Large Language Models (LLM). Their tool, known as the Large Language Model Debugger (LDB), seeks to enhance the efficacy and reliability of LLM-generated code. Using this new tool, developers can focus on discrete sections of…

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Introducing Inflection-2.5 by Inflection AI, an improved AI model that rivals global leading language models such as GPT-4 and Gemini.

Inflection AI has introduced a significant breakthrough in Large Language Models (LLMs) technology, dubbed Inflection-2.5, to tackle the hurdles associated with creating high performance and efficient LLMs suitable for various applications, specifically AI personal assistants like Pi. The main obstacle lies in developing such models with performance levels on par with leading LLMs whilst using…

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Researchers from Carnegie Mellon University Introduce ‘Echo Embeddings’: A Novel Embedding Technique Tailored to Tackle a Structural Weakness of Autoregressive Models.

Neural text embeddings are critical components of natural language processing (NLP) applications, acting as digital fingerprints for words and sentences. These embeddings are primarily generated by Masked Language Models (MLMs), but the advent of large Autoregressive Language Models (AR LMs) has prompted the development of optimized embedding techniques. A key drawback to traditional AR LM-based…

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Introducing Occiglot: A Grand-Scale European Initiative for Open-Source Creation and Growth of Extensive Language Models.

OcciGlot, a revolutionary language model introduced by a group of European researchers, aims to address the need for inclusive language modeling solutions that embody European values of linguistic diversity and cultural richness. By focusing on these values, the model intends to maintain Europe's competitive edge in academics and economics and ensure AI sovereignty and digital…

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Deciphering the ‘Intelligence of the Silicon Masses’: How LLM Groups Are Revolutionizing Forecasting Accuracy to Equate Human Prowess

Large Language Models (LLMs), trained on extensive text data, have displayed unprecedented capabilities in various tasks such as marketing, reading comprehension, and medical analysis. These tasks are usually carried out through next-token prediction and fine-tuning. However, the discernment between deep understanding and shallow memorization among these models remains a challenge. It is essential to assess…

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