Quickchat AI’s Post

When it comes to developing AI solutions for finance, good measurement of LLM effectiveness has lagged behind other fields. FinBen is a financial benchmark that measures the myriad capabilities an LLM financial analyst needs to be successful. The benchmark authors focus on real-world scenarios involving complex logical tasks. Decision-making, abstract reasoning, quantitative reasoning, and problem-solving skills are all measured. GPT-4 is the winner in quantification, extraction, understanding, and trading. Gemini gets the gold in the categories of forecasting and argument relation classification. https://lnkd.in/gB8d8Phb

📢 All You Need to Know About Financial Statement Analysis with LLM paper You might have heard about the paper on Financial Statement Analysis with Large Language Models that's creating quite a buzz. If you want a quick overview of what it says, check out the short summary created. 📄✨ In short, we can fairly say that LLMs can match or even outperform human analysis when dealing with financial statements. 📈🤖 The study finds that LLMs can predict very similarly, if not better, than humans. Not just humans, but also at par with existing AI models that are fine-tuned for this purpose. 💡🔍 This is proof of what we already believe is possible. The best part is that with GenAI, we can incorporate non-data elements like a company's goodwill, market trends, geopolitics, etc., making it an absolute predicting machine. 🌟📊🤖 A big thanks to Ruben Hassid for pointing this out to me and encouraging deeper thought on it. 🙏📚

Many companies want to implement RAG systems but being able to have an evaluation framework is pivotal in an inherently abstract space like Generative AI. I liked the approach detailed by Igor below, feel free to use as it fits in your projects!

Friends, let's shift our focus in the LLM landscape! We're caught in a thrilling race where models trade blows on benchmarks. Sure, progress is exhilarating, but remember – most of us aren't building these models. Like the CPU craze of old before multi-core came into existence, chasing the absolute 'best' LLM can distract us. Unless when you are pushing production limits, any top LLM will be more than enough for your needs. The real win? Finding the LLM that excels at solving YOUR task, balancing performance and cost. That is the strategic mindset that transforms potential into business results. What's the unique problem YOUR chosen LLM will solve? #AI #LLM #BusinessStrategy

It’s time to slow down and take a step back with Artificial Intelligence. Lest we work ourselves into a “fait accompli” situation. Read why in this opinion piece https://lnkd.in/dGe_-khr

How to Improve Performance of a Retrieval Augmented Generative (RAG) Model: Enhance your RAG model performance with advanced retrieval techniques, user-centric fine-tuning, multimodal integration, and ethical AI… Continue reading on Generative AI » #genai #generativeai #ai

Join us on September 24 for the ninth session of our AI-centered webinar series, "The Revolution Will Be Synthesized." This session will concentrate on the European Union's Artificial Intelligence Act.

"I am honored to be attending the esteemed Artificial Intelligence and Financial Markets course, where I can gain valuable insights into the applications of AI in finance and develop a deeper understanding of the future of financial markets."

The second part of your article I found most interesting and useful. It brought to my attention (which is as we now know is so important) the “Grokked Transformers are Implicit Reasoners” research paper that came out in May. The fact that this was almost 3 months ago, and your reference is the first I have seen about this, does make me ponder though how important this research may be. Never-the-less, I found it very interesting that “an extended period of training far beyond overfitting” could result in vastly superior performance. I find that at least some AI researchers have been aware of the advantages of overfitting for many years. But then the question is why are the best LLMs not doing this - as far as we know? One reason may be found in this statement from the “Grokked” paper: “…almost perfect accuracy after extended optimization lasting around 50 times the steps taken to fit the training data.” 50 times! Already the training of LLMs takes weeks if not months. If overfitting was done 50 times, training would then take years! However, with LLMs now using mixtures of experts, perhaps a subset of experts might be overfitted. In particular, it might be very beneficial for the math/logic expert to be overfitted. “Implicit reasoning”, which is what grokking improves, is Kahneman’s “System 1 thinking”. When humans do System 2 thinking we take System 1 results as input, but to do correctly we must also do a fresh look at the facts and apply our own reasoning abilities, otherwise biases in System 1 will lead to false conclusions in our System 2. But System 2 as Kahneman points out takes time and energy, for both humans and LLMs. So we and LLMs generally default to using System 1. So improvements in System 1 thinking will definitely help in the overall performance of man and machine. But on the other hand, grokking appears not to be the secret sauce needed to help LLMs to achieve expert human System 2 thinking.

What if you could quickly find important information from 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗿𝗲𝗽𝗼𝗿𝘁𝘀, earnings statements, and presentations without having to search through everything? With 𝗟𝗟𝗠𝘀, 𝗟𝗮𝗻𝗴𝗰𝗵𝗮𝗶𝗻 and 𝗥𝗔𝗚 (Retrieval-Augmented Generation), you can automatically 𝗽𝘂𝗹𝗹 𝗼𝘂𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 you need and𝗴𝗲𝘁 𝗵𝗲𝗹𝗽𝗳𝘂𝗹 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 fast. 🚀 With 𝗙𝗔𝗜𝗦𝗦 for fast similarity searches, you can do this in seconds! That means less time searching and more time focusing on smart decisions. 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗺𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/e599BYfR Let AI handle the hard work, so you can focus on making great decisions! 💼