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Closest Neighbor Conjectural Decoding (NEST): A Revision Technique Applied During Inference-Time in Language Models to Improve Accuracy and Attribution Utilizing Closest Neighbor Conjectural Decoding

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 2, 202461Views0Likes0Comments
Large Language Models (LLMs) are known for their ability to carry out multiple tasks and perform exceptionally across diverse applications. However, their potential to produce accurate information is inhibited, particularly when the knowledge is less represented in their training data. To tackle this issue, a technique known as retrieval augmentation was devised, combining information retrieval…

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Complexity of Data and Growth Rules in Neural Language Models

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 2, 202474Views0Likes0Comments
Selecting the right balance between enhancing the data set and enhancing the model parameters in a given computational budget is essential for the optimization of Neural Networks. Scaling rules assist in this allocation of strategies. Past research has recognized a 1-to-1 ratio of parameter count scaling and training token count as the most effective approach…

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This AI research conducted by Princeton and the University of Warwick suggests an innovative AI method to improve the use of LLMs as cognitive models.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJune 2, 202465Views0Likes0Comments
Large Language Models (LLMs) often exhibit judgment and decision-making patterns that resemble those of humans, posing them as attractive candidates for studying human cognition. They not only emulate rational norms such as risk and loss aversion, but also showcase human-like errors and biases, particularly in probability judgments and arithmetic operations. Despite their potential prospects, challenges…

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Adaptive Visual Tokenization in Matryoshka Multimodal Models: Boosting Efficacy and Versatility in Multimodal Machine Learning

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJune 2, 202469Views0Likes0Comments
Multimodal machine learning combines various data types such as text, images, and audio to create more accurate and comprehensive models. However, large multimodal models (LMMs), like LLaVA, have been facing problems dealing with high-resolution graphics due to their inflexible and inefficient nature. Many have recognized the necessity for methods that may adjust the number of…

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LLM360 presents K2: An entirely replicate-able, open-source Large Language Model that outperforms Llama 2 70B while using 35% less computational energy.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 2, 202455Views0Likes0Comments
K2 is an advanced large language model (LLM) by LLM360, produced in partnership with MBZUAI and Petuum. This model, dubbed K2-65B, comprises 65 billion parameters and is completely reproducible, meaning that all components, including the code, data, model checkpoints, and intermediate results, are open-source and available to anyone. The main aim of this level of…

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RobustRAG: An Exclusive Protective Structure Designed to Counteract Retrieval Pollution Attacks within Retrieval-Augmented Generation (RAG) Systems.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJune 2, 202473Views0Likes0Comments
Retrieval-augmented generation (RAG) has been used to enhance the capabilities of large language models (LLMs) by incorporating external knowledge. However, RAG is susceptible to retrieval corruption, a type of attack in which disruptive information is inserted into the document collection, leading to the generation of incorrect or misleading responses. This poses a serious threat to…

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Transitioning from Explicit to Implicit: Gradual Integration Catalyzes the Advent of a New Age in Reasoning for Natural Language Processing

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 1, 202471Views0Likes0Comments
Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. However, enhancing their ability to solve complex reasoning tasks that require logical steps and coherent thought processes is challenging, particularly as most current models rely on generating explicit intermediate steps which are computationally expensive. Several existing methods attempt to address these challenges. Explicit…

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Tackling Bootlicking in AI: Difficulties and Findings from Human Input Training

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 1, 202474Views0Likes0Comments
Researchers from the University of Oxford and the University of Sussex have found that human feedback, used to fine-tune AI assistants, can often result in sycophancy, causing the AI to provide responses that align more with user beliefs than with the truth. The study revealed that five leading AI assistants consistently exhibited sycophantic tendencies across…

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“RAG Me Up”: An Universal AI Infrastructure (Server + User Interfaces) Facilitating Personal Dataset RAG Operations with Ease

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 1, 202469Views0Likes0Comments
Managing and effectively utilizing large amounts of diverse and extensive data from various documents is a considerable challenge in the fields of data processing and artificial intelligence. Many organizations struggle with efficiently processing different types of files and formats while ensuring the accuracy and relevance of the information being extracted. These complications often lead to…

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Dir-Assistant: Streamlining File Management through Local and API Language Models

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Staff, Tech News, Technology, UncategorizedJune 1, 202488Views0Likes0Comments
The management of large files and directories can be a laborious task, often requiring substantial time and effort to navigate and locate specific information. Traditional file management and search methods are becoming increasingly ineffective in this task, as they don't always provide contextual understanding or capable summarisation. Nonetheless, various solutions like fundamental search operations and…

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Comprehending AI System Prompts and the Impact of Zero-shot versus Few-shot Prompting in Artificial Intelligence

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedJune 1, 202472Views0Likes0Comments
Within the world of Artificial Intelligence (AI), system prompts and the concepts of zero-shot and few-shot prompting have revolutionized the interaction between humans and Large Language Models (LLMs). These methods enhance the effectiveness and applicability of LLMs by guiding AI models to produce accurate and contextually appropriate responses. Essentially, system prompts serve as the initial instructions…

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