Sentiment analysis or classification 😛
AI
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Probabilistic Machine Learning: Comprehensive Guide to Generative AI
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Probabilistic #MachineLearning #GenerativeAI #AI @Khulood_Almani @Analytics_699 @GlenGilmore @MargaretSiegien @sallyeaves @baski_LA @ChuckDBrooks @HakomTimeSeries @labordeolivier @BetaMoroney @SabineVdL @KanezaDiane @MaiaGabunia @amalmerzouk https://
probml.github.io/pml-book/book2
.html?s=09
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RNN-based LLMs and information retention patterns
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This is quite interesting … 1) I would expect that the opposite is true for, e.g., RNN-based LLMs like RWKV (since it's processing information sequentially, it might rather forget early information) 3/5
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Transformer Architecture and Middle Document Retrieval Performance Bias
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2) To my knowledge, there is no specific inductive bias in transformer-based LLM architectures that explains why the retrieval performance should be worse for text in the middle of the document. 4/5
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LLM Attention Weights: Training Data Structure and Human Writing Patterns
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I suspect it is all because of the training data and how humans write: the most important information is usually in the beginning or the end (think paper Abstracts and Conclusion sections), and it's then how LLMs parameterize the attention weights during training. 5/5
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Long Context LLMs: Promise and the Middle Information Problem
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We have seen a new wave of LLMs for longer contexts: 1) RMT, 2) Hyena LLM & 3) LongNet There are several use-cases for such long LLMs but the elephant in the room is: How well do LLMs use these longer contexts? Turns out not so well if info is in the middle of the input.
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LLMs Struggle Retrieving Information from Document Middle
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According to "Lost in the Middle: How Language Models Use Long Contexts" (
https://
arxiv.org/abs/2307.03172) LLMs are good at retrieving information at the beginning of documents. They do less well in terms of retrieving information if its contained in the middle of a document. 2/5 -
ChatGPT Code Interpreter Now Available to All Plus Users
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This week, the company said that it is taking one of its own in-house plug-ins, Code Interpreter, and making it available to all of its ChatGPT Plus subscribers. Code Interpreter comes to all ChatGPT Plus users — ‘anyone can be a data analyst now’ https://
bit.ly/43vPh4l -
Entrepreneurial Community Shares Experiences and Insights
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One thing I’ve noticed is that most entrepreneurs/investors on Twitter are really kind and really forthcoming with their experiences and what has worked for them. We actually have an awesome community here.
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Bootstrapping Startups Face Rising User Acquisition Costs
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it’s almost like advertising right? I mean if you don’t own the podcast itself, you struggle to constantly hit your target users and will have to pay every time. Becomes expensive really quickly (bootstrapping the next thing therefore being very cost conscious).