Learn more about #ContentManagement with the Term of the Week. This week's choice: Large language model training A large language model (LLM) is a deep neural network that can perform a variety of natural language processing tasks. It can operate because it is trained on huge volumes of data. It is trained using natural language or human language samples. After parsing large volumes of information, the LLM can recreate answers by generating probabilities of a series of words. But an LLM is only as good, or as bad, as the data it is trained on. Continue reading: https://hubs.ly/Q02Gy8db0 #StructuredContent #SemanticAI #DITA #Metadata
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Learn more about #ContentManagement with the Term of the Week. This week's choice: Large language model training A large language model (LLM) is a deep neural network that can perform a variety of natural language processing tasks. It can operate because it is trained on huge volumes of data. It is trained using natural language or human language samples. After parsing large volumes of information, the LLM can recreate answers by generating probabilities of a series of words. But an LLM is only as good, or as bad, as the data it is trained on. Continue reading: https://hubs.ly/Q02HnS940 #StructuredContent #SemanticAI #DITA #Metadata #LLM #LLMS
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Hello connections! I am very happy to share another exciting task which I completed in Hugging face under the natural language processing session which is Fill Mask. Masked language modeling is the task of masking some of the words in a sentence and predicting which words should replace those masks. These models are useful when we want to get a statistical understanding of the language in which the model is trained in. #APSCHE #AIMERS #AIMERS Society #Hugging face #Fill Mask
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Retrieval Augmented Generation (#RAG) systems have become increasingly popular in natural language processing tasks, offering a powerful combination of large language models and external knowledge retrieval. However, the effectiveness of RAG systems heavily depends on the accuracy of their retrieval component. In this article, we’ll compare three techniques to enhance retrieval accuracy: #reranking, #ranknormalization, and #rankfusion. https://lnkd.in/eapYKjgr
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Brand partnership • Quality Assurance Engineer II | Amazon | Alexa | Ex - Oracle | Ex - DXC | AI | LLM | NLP
Just completed an insightful course on Large Language Models (LLMs) Excited to apply this knowledge in my work and explore opportunities to leverage LLMs for innovative solutions. Let's connect if you're interested in discussing the fascinating world of natural language processing and AI! #LLMs #NaturalLanguageProcessing #AI #MachineLearning #ProfessionalDevelopment
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PRE-FINAL YEAR||CLOUD ENTHUSIAST || GDSC member @SJCE || I am a sophomore majoring Artificial Intelligence and Data science
Title: "Decoding Emotions: Analyzing Movie Reviews with Sentiment Analysis" Dive into the world of sentiment analysis as we explore the highs and lows of movie reviews using techniques like Natural Language Processing and Machine Learning. #CognoRise #SentimentAnalysis
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Today's word: BERT. BERT (Bidirectional Encoder Representations from Transformers) is a popular language model that is used in natural language processing. Click the link in the comments section to learn more..
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Language processing has been around long before the age of AI ! Emacs includes a built-in game called "Doctor". This game is actually an implementation of the famous ELIZA program, created by Joseph Weizenbaum in the mid-1960s. ELIZA was an early natural language processing computer program. It simulates a conversation with a Rogerian psychotherapist and can be surprisingly entertaining. To access this feature in Emacs, all you need to do is type M-x doctor and start chatting! #LanguageProcessing #Emacs #ELIZA #AI #FunFacts
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Large language models (LLMs) are revolutionizing natural language processing. Learn how to build safer AI applications using guardrails on Amazon Bedrock in this article by Nikola Kucerova. #LLM #AI #AmazonBedrock #GenerativeAI #ResponsibleAI #this_post_was_generated_with_ai_assistance https://lnkd.in/emUt7gGX
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The most interesting thing in tech: Can AI systems come up with novel ideas? A new paper advances the debate with a clever experiment in the field of natural language processing. Experts rated the ideas generated by three groups: A) human researchers B) AI systems that came up with ideas and ranked them themselves C) AI systems that came up with ideas that were then ranked by humans.The winner? Group C. (And B did better than A.) It's an interesting data point in an ever-interesting debate. Yes, these systems are just word-prediction machines that hallucinate all the time. But if used correctly, they can lead to original insights too.
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Interesting. Given the information demand to create ideas in the modern world, it's only a matter of time before AI overtakes us in this capability. For me, the real question is whether we have the training data sets that would provide to real world value as an output and not noise, given that over 50% of the publicly available content is already AI-generated (by the previous, more imperfect LLM's) and is thus on a declining quality curve and on an increasing volume curve. #artificialintelligence #creativity #genai
The most interesting thing in tech: Can AI systems come up with novel ideas? A new paper advances the debate with a clever experiment in the field of natural language processing. Experts rated the ideas generated by three groups: A) human researchers B) AI systems that came up with ideas and ranked them themselves C) AI systems that came up with ideas that were then ranked by humans.The winner? Group C. (And B did better than A.) It's an interesting data point in an ever-interesting debate. Yes, these systems are just word-prediction machines that hallucinate all the time. But if used correctly, they can lead to original insights too.
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