Nvidia has unveiled a RAG-based Retriever service to stop generative AI hallucinating inaccurate responses.
The NeMo Retriever is a generative AI microservice that enables enterprises to connect custom large language models to enterprise data, thereby delivering accurate responses to an AI application user’s requests and questions. It essentially involves semantic retrieval, with retrieval-augmented generation (RAG) capabilities that utilize Nvidia algorithms. It is part of Nvidia’s AI Enterprise software platform and is available in the AWS Marketplace.
Jensen Huang, Nvidia co-founder and CEO, stated: “Generative AI applications with RAG capabilities are the next killer app of the enterprise. With Nvidia NeMo Retriever, developers can create customized generative AI chatbots, copilots and summarization tools that can access their business data to transform productivity with accurate and valuable generative AI intelligence.”
NeMo Retriever supports production-ready generative AI with commercially viable models, API stability, security patches, and enterprise support, unlike open source RAG toolkits. It contains embedding models that capture relationships between words, enabling LLMs (large language models, the technology underlying generative AI) to process and analyze textual data.
Nvidia claims that enterprises can connect their LLMs to multiple data sources – such as text, PDFs, images, videos, and knowledge bases – so that users can interact with data and receive accurate, up-to-date answers using simple, conversational prompts.
It asserts that enterprises can use NeMo Retriever to achieve more accurate results with less training, speeding up time to market and supporting energy efficiency in the development of generative AI applications.
Cadence (industrial electronics design), Dropbox, SAP, and ServiceNow are collaborating with Nvidia to integrate production-ready RAG capabilities into their custom generative AI applications and services.
Retrieval-augmented generation is a way of minimizing hallucinatory generative AI chatbot LLM responses from underlying LLMs trained on a general trove of information rather than domain-specific data. It works by turning a user’s request into a set of coded parameters called vector embeddings. These are then used to query a domain-specific database of information local or specific to the user’s organization. This database contains vector embeddings of data relevant to the organization and the RAG query looks for similarities between the input request vector embeddings and the database’s vector embedded content, choosing the content with the closest similarity.
Minimising hallucination isn’t the same as eliminating it, though. The RAG process requires verification to ensure the output is contextually relevant, grounded, and accurate. Vectara has developed an open source Hallucination Evaluation Model to assist in this verification.
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What is AI Hallucination
AI hallucination is a phenomenon in which an AI system generates outputs that contain false or misleading information presented as fact. This can occur when the AI system lacks real-world understanding, has inherent biases, or is limited by training data 123. For example, a chatbot might generate a response that contains a plausible-sounding random falsehood 1. The term “hallucination” is used in loose analogy with the phenomenon of hallucination in human psychology, but one key difference is that human hallucination is usually associated with false percepts, while an AI hallucination is associated with unjustified responses or beliefs 1. AI hallucinations have been criticized for creating unreliable or nonsensical content 12.
What is Vector Embedding
Vector embeddings are lists of numbers that represent real-world objects and concepts as dense vectors. They are useful for various machine learning tasks such as clustering, recommendation, and classification. Vector embeddings are one of the most fascinating and useful concepts in machine learning. They are central to many NLP, recommendation, and search algorithms. If you’ve ever used things like recommendation engines, voice assistants, language translators, you’ve come across systems that rely on embeddings. ML algorithms, like most software algorithms, need numbers to work with. Sometimes we have a dataset with columns of numeric values or values that can be translated into them (ordinal, categorical, etc). Other times we come across something more abstract like an entire document of text. We create vector embeddings, which are just lists of numbers, for data like this to perform various operations with them. A whole paragraph of text or any other object can be reduced to a vector. Even numerical data can be turned into vectors for easier operations. But there is something special about vectors that makes them so useful. This representation makes it possible to translate semantic similarity as perceived by humans to proximity in a vector space. In other words, when we represent real-world objects and concepts such as images, audio recordings, news articles, user profiles, weather patterns, and political views as vector embeddings, the semantic similarity of these objects and concepts can be quantified by how close they are to each other as points in vector spaces. Vector embedding representations are thus suitable for common machine learning tasks such as clustering, recommendation, and classification123
Blocks and Files Nvidia RAG-based Retriever service stop generative AI hallucinating inaccurate responses AI AI Chat Bots Vector Embedding
