This 45-minute webinar from Ad Age, Zuora and David Warren will examine how top media companies are using a wealth of historical knowledge to create advanced language models. Register today to learn more! Made possible by Zuora. #ad https://lnkd.in/g_9jS4J7
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Join our webinar “Introduction to LLMs” with Mahathi Bhagavatula! Don't miss this opportunity to unravel the mysteries behind Large Language Models. Date: 29th January 2024 Time: 7:00PM Register now: https://bit.ly/3Sxz1NC #LLMs #AIwebinar #techwebinar #LargeLanguageModels #LLMwebinar
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How can we identify which value of an embedding correlates with each feature in the human language? https://lnkd.in/g_pjvzan
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Rapid adoption of large language models shows the value of language as the new interface. After all, it's only natural that we communicate in our natural language but it's essential that interface 'understands' us. CEO, Phil Finucane talks about today's tools enterprise tools that CTOs are faced with with some practical advice from lived experience. Tune in also for what's coming from Pat Inc to support the new interface of language #RRGlinguistics #patomtheory https://lnkd.in/gPR8tQ_V
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In this episode, we discuss Branch-Solve-Merge Improves Large Language Model Evaluation and Generation by Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, Mohit Bansal, Jason Weston, @Xian Li. The paper introduces the BRANCH-SOLVE-MERGE (BSM) method for improving Large Language Models (LLMs). This method enhances task planning and coherence in LLMs by breaking tasks into sub-tasks, solving them separately, and then combining the solutions. BSM has shown significant improvements in response evaluation and constrained text generation, including better alignment with human judgment, reduced biases, and higher constraint satisfaction.
arxiv preprint - Branch-Solve-Merge Improves Large Language Model Evaluation and Generation
podbean.com
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Deciphering Doubt: Navigating Uncertainty in LLM Responses This paper explores the domain of uncertainty quantification within large language models
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📢 First session of the day is about to start! 📌 Fine-tuning Large Language Models for the Larger World 👉 Tap the link to join: https://lnkd.in/dTQnS-Ec
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Adaptive-RAG: Enhancing Large Language Models by Question-Answering Systems with Dynamic Strategy Selection for Query Complexity In the evolving field of Retrieval-Augmented Generation
Adaptive-RAG: Enhancing Large Language Models by Question-Answering Systems with Dynamic Strategy Selection for Query Complexity
openexo.com
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Dictionary.com has chosen "hallucinate" as its word of the year for 2023. “Hallucinate as our 2023 Word of the Year encapsulates technology’s continuing impact on social change, and the continued discrepancy between the perfect future we envision and the messy one we actually achieve.”, Grant Barrett, Head of Lexicography 💡I like the ambiguous perspective presented by Grant Barrett, who contrast a traditional reality with the inherent flaws of large language models (LLMs). The word was selected based on the site's search data, language trends, and major cultural themes of the year. It is comparable to other tech terms that initially had different meanings, such as "spam" and "virus". #artificialintelligence #wordoftheyear
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Data & AI Professional | Championing Pristine Data and AI Innovation | Architect | Pianist | Martial Artist
Importance of enough quantity of “quality” data in training LLMs…
Awesome poster presentation by Niklas Muennighoff for the paper "Scaling Data-Constrained Language Models" at #NeurIPS2023 Kudos Sasha Rush Boaz Barak Teven Le Scao Aleksandra Piktus Nouamane Tazi Sampo Pyysalo Thomas Wolf Colin Raffel
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