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.

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

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

Why do the GNNs A*Net and NBFNet achieve superior performance for Knowledge Graph Completion? We found evidence that approximately half of the difference to a rule-based approach is due to negative patterns exploited by the GNNs. 💡 In the example graph, the models have to predict Bobby as the best answer to the question "Who follows Anna?". ❓ But what exactly can be learned from this very simplistic graph? The trick is in learning to penalize all other answer possibilities. More details are given in the paper! Preprint: https://lnkd.in/e37Tf8Hk

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

"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.

A great clarifying challenge: to insert the Old and New Testaments into an Artificial Intelligence to detect consistencies, contradictions, discrepancies, what is impossible to do so far, etc..... within all the books that compose them.   

Zhide Wang and I are drafting up a sequel to “Identification of Structural Learning Models” (https://lnkd.in/e2nTbtf3) titled “Counterfactual (Non)Identification of Structural Learning Models”… stay tuned for paper link, hopefully soon! Punch line: Model primitives in learning models can be identified (first paper), but it’s more nuanced when we think about counterfactuals (second paper)…