Thoughts on LLMs use cases
Thoughts on LLMs use cases
Cool and less glamorous use cases
Cool and less glamorous use cases
Sep 4, 2023
While everybody is concentrating on cool complex use cases like RAGs or Agents, LLMs would be a revolution by themselves only considering less glamorous use cases like text classification and text extraction. Social Media are full with the latest advancements in chatbots and agents, but the productivity gains in less glamorous text classification and extraction brought about by LLMs are already a real game-changer.
Text Classification
Before LLMs: The journey of developing a text classifier was arduous and time-consuming. It involved manual data collection, labeling, feature design, and selection, and choosing a suitable machine learning model. This intricate process required substantial expertise and resources.
With LLMs: Today, the landscape has transformed. You can instruct an LLM to classify texts by writing a simple prompt. The LLM handles feature extraction and model selection, drastically reducing the time and expertise needed. This revolution allows for rapid development, iteration, and enhanced results, making the process more efficient and resource-effective.
Entity Extraction
Before LLMs: Extracting entities like names and addresses from documents was a labor-intensive task. It required creating specific algorithms or rules tailored to the text’s structure and content, demanding a deep understanding of the text data.
With LLMs: Now, you can guide the model to identify and extract desired entities with a brief prompt. LLMs understand and process natural language, automatically identifying relevant entities without extensive algorithm development or rule-writing. This advancement has streamlined the entity extraction process, making it more accessible and efficient.
In both text classification and entity extraction, efficiency is the keyword. LLMs have brought about a significant enhancement in the efficiency of these processes, making them faster and more accessible without the need for deep technical expertise.
By embracing the LLM revolution, we can unlock unprecedented opportunities in text classification and extraction, paving the way for more advanced and efficient text processing capabilities in various applications and industries.
While everybody is concentrating on cool complex use cases like RAGs or Agents, LLMs would be a revolution by themselves only considering less glamorous use cases like text classification and text extraction. Social Media are full with the latest advancements in chatbots and agents, but the productivity gains in less glamorous text classification and extraction brought about by LLMs are already a real game-changer.
Text Classification
Before LLMs: The journey of developing a text classifier was arduous and time-consuming. It involved manual data collection, labeling, feature design, and selection, and choosing a suitable machine learning model. This intricate process required substantial expertise and resources.
With LLMs: Today, the landscape has transformed. You can instruct an LLM to classify texts by writing a simple prompt. The LLM handles feature extraction and model selection, drastically reducing the time and expertise needed. This revolution allows for rapid development, iteration, and enhanced results, making the process more efficient and resource-effective.
Entity Extraction
Before LLMs: Extracting entities like names and addresses from documents was a labor-intensive task. It required creating specific algorithms or rules tailored to the text’s structure and content, demanding a deep understanding of the text data.
With LLMs: Now, you can guide the model to identify and extract desired entities with a brief prompt. LLMs understand and process natural language, automatically identifying relevant entities without extensive algorithm development or rule-writing. This advancement has streamlined the entity extraction process, making it more accessible and efficient.
In both text classification and entity extraction, efficiency is the keyword. LLMs have brought about a significant enhancement in the efficiency of these processes, making them faster and more accessible without the need for deep technical expertise.
By embracing the LLM revolution, we can unlock unprecedented opportunities in text classification and extraction, paving the way for more advanced and efficient text processing capabilities in various applications and industries.