Machine Learning - IAEE’s Post

To enhance the performance of LLMs in text-generation tasks, Roman Smirnov outlines the benefits and inner workings of classifier-free guidance.

Refine your retrieval strategies for better results on your RAG pipeline 📈 🛠️ Change the Chunking Strategy 📦 Change the Embedding Model 🔍 Change the LLM Model 🤖 Learn how: https://lnkd.in/gQDdYUVn

https://lnkd.in/gVBAiVZS A new exciting paper exploring how to push past the limits of language for reasoning. Instead of using words to represent each reasoning step, Coconut uses hidden states of the model as a "continuous thought," feeding these directly back into the model to guide further reasoning. This allows the model to explore multiple potential reasoning paths and make more effective decisions, especially in tasks that require revisiting earlier steps. The method improves performance in some logical reasoning tasks, showing that reasoning in this "latent" space can offer new possibilities for model performance.

"With Matryoshka embeddings, you can change the dimension of your embeddings depending on your application. This can reduce storage space, save costs, and increase retrieval speed." Dr. Leon Eversberg expands on MRL in his recent article.

Building with LLMs for production. A very approximate graph based on my little, empirical experience. Applies not only in this field but especially in it.

Here's the first article with my son about the computational algorithm of Relative Resolution https://lnkd.in/gACGbpea

There is this beautiful tool called VoSS (https://lnkd.in/gQP23VuZ) which is used for describing visualising, analysing and proving properties about integrated circuits. Boolean functions are the first-class objects in it which get represented as ordered binary decision diagrams internally. I then wondered if I could know more on this beautiful import from CS called BDDs which are the backbone of such an important tool. Sure enough a google search away was Knuth's take on it :D https://lnkd.in/gChPj7JX

This time on my journey to make cool stuff, I automated my YouTube title creation using Google’s new open source LLM, Gemma 2. In an experiment to push the limits of the 9 billion parameter model and see just how well the latest state of the art local LLM can perform, I gave it the complex task of analyzing an example YouTube channel, giving me a report on the techniques and strategies they use in their video titles, then applying those learnings to create new titles about any topic. All the while, Gemma reflects on what its done at each step and either accepts its output or gives itself additional instructions to improve and retries, allowing us to fully test its ability to synthesize, reason, and generate. Check out how the model performs and whether or not Gemma will become the new standard of local language models in my latest video here (and yes, the title of this video was created from this experiment too!) https://lnkd.in/e2t4pun2

Came across a really interesting paper on Multi-token prediction. So far we've seen LLMs predicting only the "next-token" in the sequence, but this paper really builds on first principles, to predict "multiple-tokens" in the sequence. This way the inference time is significantly reduced! Interesting fact is the performance gain increases with the model size. Link to paper: https://lnkd.in/gKvZ2w9s

Excited to share our first paper on ML! (though just a short WP)