{"id":117378,"date":"2023-11-14T19:00:00","date_gmt":"2023-11-15T00:00:00","guid":{"rendered":"https:\/\/www.dacgroup.com\/crafting-precision-content-using-large-language-models\/"},"modified":"2024-12-20T09:24:53","modified_gmt":"2024-12-20T14:24:53","slug":"crafting-precision-content-using-large-language-models","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-ca\/insights\/blog\/strategy\/crafting-precision-content-using-large-language-models\/","title":{"rendered":"Crafting precision content using large language models"},"content":{"rendered":"

The latest Large Language Models<\/span> (LLM)<\/span>, although toddlers, are in many ways much smarter than the average human is\u2014or will ever be. Not only do they know more, but they have a much greater capacity to process and act on vast arrays of instructions.\u00a0<\/span>\u00a0<\/span><\/p>\n

We\u2019ve recently explored the potential of LLMs to interpret or generate text considering numerous parameters with extraordinary precision. We have demonstrated their ability to evaluate unstructured text blocks and grade them along various scales to great success. Additionally, we have been able to generate specific, tailored content using a complex series of instructions that can be tuned with precision to the same set of scales.<\/span>\u00a0<\/span><\/p>\n

We are entering a new era of performance-based, data-driven content where we can transform LLMs into written content production facilities, complete with a control board of knobs and dials to numerically fine-tune optimal marketing content across various formats.\u00a0<\/span>\u00a0<\/span><\/p>\n

Here, we share our initial experiment findings and offer a general methodology to harness LLMs to extract quantitative measures from otherwise unstructured text, opening the door to statistical analysis and optimization of content creation.<\/span>\u00a0<\/span><\/p>\n

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Source: Analytics Vidhya<\/p>\n

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Large language models are perfectly suited to plot ideas<\/h2>\n

Large language models are constructed using mathematical vectors. This foundation allows them to adeptly translate blocks of text\u2014rating, scoring, or mapping content into numeric values that capture the distinct qualities of each block. For example, we can ask the model to evaluate how \u2018technical\u2019 this blog is on a scale of 1-10, where the boundaries are defined by example or by user instruction. Similarly, LLMs can reverse this direction to generate blocks of text that correspond to numeric scores of a certain attribute submitted by a user. For example, you can ask the LLM to write a blog post with the degree of technicality scoring 9 out of 10.<\/span>\u00a0<\/span><\/p>\n

We can additively include more concepts to build a conceptual space. This is what we term a conceptual Cartesian space, which the LLM can refer to for content generation. We can plot a point in this space to define an idea based on its position relative to each of the axes that define our space.\u00a0<\/span>\u00a0<\/span><\/p>\n

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Source: https:\/\/serokell.io\/blog\/language-models-behind-chatgpt<\/p>\n

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