Practical Uses of Language Models

Focusing on AI’s hype might cost your company millions… (Here’s what you’re overlooking) Every week, new AI tools grab attention—whether it’s copilot assistants or image generators. While helpful, these often overshadow the true economic driver for most companies: AI automation. AI automation uses LLM-powered solutions to handle tedious, knowledge-rich back-office tasks that drain resources. It may not be as eye-catching as image or video generation, but it’s where real enterprise value will be created in the near term. Consider ChatGPT: at its core, there is a large language model (LLM) like GPT-3 or GPT-4, designed to be a helpful assistant. However, these same models can be fine-tuned to perform a variety of tasks, from translating text to routing emails, extracting data, and more. The key is their versatility. By leveraging custom LLMs for complex automations, you unlock possibilities that weren’t possible before. Tasks like looking up information, routing data, extracting insights, and answering basic questions can all be automated using LLMs, freeing up employees and generating ROI on your GenAI investment. Starting with internal process automation is a smart way to build AI capabilities, resolve issues, and track ROI before external deployment. As infrastructure becomes easier to manage and costs decrease, the potential for AI automation continues to grow. For business leaders, identifying bottlenecks that are tedious for employees and prone to errors is the first step. Then, apply LLMs and AI solutions to streamline these operations. Remember, LLMs go beyond text—they can be used in voice, image recognition, and more. For example, Ushur is using LLMs to extract information from medical documents and feed it into backend systems efficiently—a task that was historically difficult for traditional AI systems. (Link in comments) In closing, while flashy AI demos capture attention, real productivity gains come from automating tedious tasks. This is a straightforward way to see returns on your GenAI investment and justify it to your executive team.

Large language models: a primer for economists (https://lnkd.in/eJschCjr) & Systematic Interpretation of Central Bank Communication Large Language Models (LLMs) have revolutionized economic research by enabling advanced analysis of unstructured textual data such as policy statements, financial reports, and news articles. These models transform text into structured numerical representations, facilitating tasks like sentiment analysis, forecasting, and topic modeling. Their contextual understanding, enabled by transformer-based architectures, makes them particularly effective in analyzing economic narratives. For instance, LLMs can evaluate market sentiment or interpret the tone of central bank communications, offering valuable insights into monetary policy impacts. A study of US equity markets demonstrated this by analyzing over 60,000 news articles to identify key drivers such as fundamentals, monetary policy, and market sentiment, linking these themes to stock market movements. Before the explosion of LLMs, I conducted research with my colleagues at Morgan Stanley to systematically analyze central bank communication using earlier machine-learning techniques. Specifically, we trained a Convolutional Neural Network (CNN) to assess the degree of hawkishness or dovishness in FOMC communications. This effort led to the development of the MNLPFEDS Index, which proved to be a powerful tool for anticipating monetary policy actions up to a year in advance. The index provided valuable insights into potential inflection points in the monetary cycle and their effects on rates, the yield curve, and the USD. This work highlighted the predictive power of communication analysis, even before the advent of the sophisticated transformer models now driving advancements in LLMs. LLMs and earlier machine-learning approaches, like CNN-based analysis, each bring unique strengths to understanding monetary policy and market dynamics. While LLMs excel in processing vast and complex datasets with contextual depth, their capabilities can be further enhanced through fine-tuning for domain-specific tasks. This adaptability allows LLMs to specialize in areas like central bank communication, where nuances in tone and context are crucial. Combined with the foundational contributions of earlier models like the MNLPFEDS Index, fine-tuned LLMs provide economists with a comprehensive toolkit to analyze qualitative insights and integrate them into robust quantitative frameworks, enriching the understanding of policy effects and broader economic trends. #EconomicResearch #MonetaryPolicy #CentralBankCommunication #MachineLearning #ArtificialIntelligence #NaturalLanguageProcessing #LLMs #DeepLearning #EconomicForecasting #SentimentAnalysis #TextAnalysis #DataScience #MacroEconomics #QuantitativeResearch

Building your own large language model is difficult and expensive but can be very rewarding. Perhaps you will find buried treasure! Charlee.ai has analyzed 55 MILLION insurance claims. After building and training their own LLM they said, “We’ve got the smartest insurance claims adjuster, ever!” By comparing a new claim against the LLM they can predict chance of litigation, claim severity and send the claim to the right adjuster. A large hospital has 100 years of patient records moldering in a warehouse. They are digitizing and preparing to analyze them stating, “Why people were cured is buried in those records”. Another company I’ve met analyzed hundreds of thousands of customer sales chats and now watches agents chat in real-time. Detecting that it is time to close the sale it makes a suggestion of what the agent should say based on its vast experience with great success. I asked the company how they ingested the company’s product catalog. “We didn’t”, they said, “We just read all the chats!” Combining your data with public data can yield new and surprising results. A hospital combined patient, appointment, prescription, weather and pollen data to forecast when patients might have to be hospitalized when pollen levels soared. They reached out to those who had let prescriptions lapse, prompted patients to be proactive during the weather event and even encouraged some to come in to the hospital early to avoid emergency services. The most interesting AI uses may well be a combination of your data, industry data and public data deployed in a new recipe that creates a new product. What hidden gems are lying in your dusty data warehouse that AI could turn into revenue?