Skip to content Skip to sidebar Skip to footer

AI Shorts

Arena Learning: Enhancing the efficiency and performance of large language models’ post-training through AI-powered simulated battles for improved natural language processing outcomes.

Large Language Models (LLMs) have transformed our interactions with AI, notably in areas such as conversational chatbots. Their efficacy is heavily reliant on high-quality instruction data used post-training. However, the traditional ways of post-training, which involve human annotations and evaluations, face issues such as high cost and limited availability of human resources. This calls for…

Read More

Arena Learning: Enhancing Efficiency and Performance in Natural Language Processing by Revolutionizing Post-Training of Broad Scale Language Models through AI-driven Simulated Contests

Large language models (LLMs) have significantly advanced our capabilities in understanding and generating human language. They have been instrumental in developing conversational AI and chatbots that can engage in human-like dialogues, thus improving the quality of various services. However, the post-training of LLMs, which is crucial for their efficacy, is a complicated task. Traditional methods…

Read More

The Branch-and-Merge Technique: Improving Language Adaptification in AI Models by Reducing Devastating Memory Loss and Guaranteeing Preservation of Fundamental Language Skills during Acquisition of New Languages.

The technique of language model adaptation is integral in artificial intelligence as it aids in modifying large pre-existing language models to function effectively across a range of languages. Notwithstanding their remarkable performance in English, these language learning models' (LLM) capabilities tend to diminish considerably when adapted to less familiar languages. This necessitates the implementation of…

Read More

Samsung Scientists present LoRA-Guard: A method of adjusting guardrails effectively using parameters, based on information exchange between LLMs and Guardrail Models.

Language models are advanced artificial intelligence systems that can generate human-like text, but when they're trained on large amounts of data, there's a risk they'll inadvertently learn to produce offensive or harmful content. To avoid this, researchers use two primary methods: first, safety tuning, which is aligning the model's responses to human values, but this…

Read More

Five Stages of Artificial Intelligence According to OpenAI: A Guide to Reaching Human-Equivalent Problem-Solving Skills

OpenAI has launched a new five-level classification framework to track its progress toward achieving Artificial Intelligence (AI) that can surpass human performance, augmenting its already substantial commitment to AI safety and future improvements. At Level 1 - "Conversational AI", AI models like ChatGPT are capable of basic interaction with people. These chatbots can understand and respond…

Read More

Ten years of Change: The Redefinition of Stereo Matching through Deep Learning in the 2020s

Stereo matching, a fundamental aspect of computer vision for nearly fifty years, involves the calculation of disparity maps from two corrected images. Its application is critical to multiple fields including autonomous driving, robotics and augmented reality. Existing surveys categorise end-to-end architectures into 2D and 3D based on cost-volume computation and optimisation methodologies. These surveys highlight…

Read More

Unveiling Q-GaLore: A Resource-Efficient Method for Initial Training and Optimization of Machine Learning Models

Large Language Models (LLMs) have become essential tools in various industries due to their superior ability to understand and generate human language. However, training LLMs is notably resource-intensive, demanding sizeable memory allocations to manage the multitude of parameters. For instance, the training of the LLaMA 7B model from scratch calls for approximately 58 GB of…

Read More

Korvus: A Comprehensive Open-Source RAG (Retrieval-Augmented Generation) Framework Designed for Postgres

The Retrieval-Augmented Generation (RAG) pipeline is a four-step process that includes generating embeddings for queries and documents, retrieving relevant documents, analyzing the retrieved data, and generating the final answer response. Utilizing machine learning libraries like HuggingFace for generating embeddings and search engines like Elasticsearch for document retrieval, this process could be potentially cumbersome, time-consuming, and…

Read More

Korvus: A Comprehensive Open-Source RAG (Retrieval-Augmented Generation) Framework Designed for Postgres

The Retrieval-Augmented Generation (RAG) pipeline is a complex process that involves generating embeddings for queries and documents, retrieving relevant documents, analyzing the retrieved data, and generating the final response. Each step in the pipeline requires its unique set of tools and queries, making the process intricate, time-consuming, and prone to errors. The development of the RAG…

Read More

Improving LLM Dependability: The Retrospective Viewpoint Method for Detecting Hallucinations

Large Language Models (LLMs) such as GPT-4 are highly proficient in text generation tasks including summarization and question answering. However, a common problem is their tendency to generate “hallucinations,” which refers to the production of factually incorrect or contextually irrelevant content. This problem becomes critical when it occurs despite the LLMs being given correct facts,…

Read More

Improving LLM Trustworthiness: The Retrospective Viewpoint Method for Identifying Hallucinations

Large language models (LLMs) such as GPT-4 have shown impressive capabilities in generating text for summarization and question answering tasks. But these models often “hallucinate,” or produce content that is either contextually irrelevant or factually incorrect. This is particularly concerning in applications where accuracy is crucial, such as document-based question answering and summarization, and where…

Read More

Hyperion: An Innovative, Modular Framework for High-Performance Optimization Tailored for Both Discrete and Continuous-Time SLAM Applications

The positioning and tracking of a sensor suite within its environment is a critical element in robotics. Traditional methods known as Simultaneous Localization and Mapping (SLAM) confront issues with unsynchronized sensor data and require demanding computations, which must estimate the position at distinct time intervals, complicating the handling of unequal data from multiple sensors. Despite…

Read More