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Language Model

The Covert Risk in AI Models: The Effect of a Space Character on Safety

Large Language Models (LLMs) are advanced Artificial Intelligence tools designed to understand, interpret, and respond to human language in a similar way to human speech. They are currently used in various areas such as customer service, mental health, and healthcare, due to their ability to interact directly with humans. However, recently, researchers from the National…

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NVIDIA presents RankRAG: An innovative RAG structure that uses a single LLM to tune-instructions for dual uses, namely top-k context ranking, and answer generation in RAG.

Retrieval-augmented generation (RAG) is a technique that enhances large language models’ capacity to handle specific expertise, offer recent data, and tune to specific domains without changing the model’s weight. RAG, however, has its difficulties. It struggles with handling different chunked contexts efficiently, often doing better with a lesser number of highly relevant contexts. Similarly, ensuring…

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This AI investigation by Tenyx delves into the cognitive abilities of Large Language Models (LLMs) by observing their understanding of geometric principles.

Large language models (LLMs) have demonstrated impressive performances across various tasks, with their reasoning capabilities playing a significant role in their development. However, the specific elements driving their improvement are not yet fully understood. Current strategies to enhance reasoning focus on enlarging model size and expanding the context length via methods such as chain of…

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This AI study by Tenyx investigates the logical capabilities of Large Language Models (LLMs) based on their understanding of geometric concepts.

Large language models (LLMs) have made remarkable strides in many tasks, with their capacity to reason forming a vital aspect of their development. However, the main drivers behind these advancements remain unclear. Current measures to boost reasoning primarily involve increasing the model's size and extending the context length with methods such as the chain of…

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An Extensive Comparison by Innodata: Evaluating Llama2, Mistral, Gemma, and GPT in terms of Accuracy, Offensive Language, Prejudice, and Tendency to Imagine

A recent study by Innodata assessed various large language models (LLMs), including Llama2, Mistral, Gemma, and GPT for their factuality, toxicity, bias, and hallucination tendencies. The research used fourteen original datasets to evaluate the safety of these models based on their ability to generate factual, unbiased, and appropriate content. Ultimately, the study sought to help…

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Innodata’s Extensive Comparisons of Llama2, Mistral, Gemma and GPT in terms of Accuracy, Harmful Language, Prejudice, and Inclination towards Illusions

An in-depth study by Innodata evaluated the performance of various large language models (LLMs) including Llama2, Mistral, Gemma, and GPT. The study assessed the models based on factuality, toxicity, bias, and propensity for hallucinations and used fourteen unique datasets designed to evaluate each model's safety. One of the main criteria was factuality, the ability of the…

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The study conducted on Artificial Intelligence by Ohio State University and Carnegie Mellon University delves into the concept of under-the-radar reasoning in Transformers and obtaining generalization via the process of grasping or Grokking.

Recent research by scientists at Ohio State University and Carnegie Mellon University has analyzed the limitations of large language models (LLMs), such as GPT-4, and their limitations in implicit reasoning. This refers to their ability to make accurate comparisons of internalized facts and properties, even when aware of the entities in question. The study focused…

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Salesforce Research has launched INDICT, an innovative framework designed to boost the security and usefulness of AI-produced coding across a wide range of programming languages.

The use of Large Language Models (LLMs) for automating and assisting in coding holds promise for improving the efficiency of software development. However, the challenge is ensuring these models produce code that is not only helpful but also secure, as the code generated could potentially be used maliciously. This concern is not theoretical, as real-world…

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This AI Article from Cohere for AI provides an exhaustive analysis about optimizing preferences in multiple languages.

The study of multilingual natural language processing (NLP) is rapidly progressing, seeking to create language models capable of interpreting and generating text in various languages. The central goal of this research is to improve global communication and access to information, making artificial intelligence technologies accessible across diverse linguistic backgrounds. However, creating such models brings significant challenges,…

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T-FREE: An Efficient and Scalable Method for Text Encoding in Large Language Models that Doesn’t Require a Tokenizer

Natural language processing (NLP) is a field in computer science that seeks to enable computers to interpret and generate human language. This has various applications such as machine translation and sentiment analysis. However, there are limitations and inefficiencies with conventional tokenizers employed in large language models (LLMs). These tokenizers break down text into subwords, demanding…

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Tsinghua University Unveils Open-Sourced CodeGeeX4-ALL-9B: An Innovative Multilingual Code Generation Model Surpassing Key Rivals and Enhancing Code Assistance.

The Knowledge Engineering Group (KEG) and Data Mining team at Tsinghua University have revealed their latest breakthrough in code generation technology, named CodeGeeX4-ALL-9B. This advanced model, a new addition in the acclaimed CodeGeeX series, is a ground-breaking achievement in multilingual code generation, raising the bar for automated code generation efficiency and performance. A product of extensive…

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