Using Large Language Models as Recommendation Systems
<p>Large Language Models (LLMs) have taken the data science community and the news cycle by storm these past few months. Since the advent of the transformer architecture in 2017, we’ve seen exponential advancements in the complexity of natural language tasks that these models can tackle from classification, to intent & sentiment extraction, to generating text eerily similar to humans.</p>
<p>From an application standpoint, the possibilities seem endless when combining LLMs with various existing technologies, to cover their pitfalls (one of my favorite being the <a href="https://writings.stephenwolfram.com/2023/03/chatgpt-gets-its-wolfram-superpowers/" rel="noopener ugc nofollow" target="_blank">GPT + Wolfram Alpha combo</a> to handle math and symbolic reasoning problems).</p>
<p><a href="https://towardsdatascience.com/using-large-language-models-as-recommendation-systems-49e8aeeff29b"><strong>Website</strong></a></p>