What Is a Large Language Model and What Can LLMs Do for Data Science?

Large language models (LLMs) are best known for their ability to generate written text and other content in human-like ways. But the usefulness of these artificial intelligence (AI) algorithms extends far beyond their uncanny ability to clearly explain a complex topic or create new songs in the style of popular artists. In the field of data science, large language models can potentially transform how teams source, manage, and analyze data. In this article, we’ll focus on how LLMs are transforming data search, changing the way data scientists ask questions and retrieve information. 

What Is a Large Language Model in AI?

Large language models (LLMs) are advanced AI systems designed to understand human language intricacies and generate intelligent, creative responses to queries. Successful LLM are trained on enormous data sets typically measured in petabytes. This training data is sourced from books, articles, websites, and other text-based sources. 

Using deep learning techniques, these models excel at understanding and generating text similar to human-produced content. Large language models power many modern applications, including content creation tools, language translation apps, customer service chatbots, financial analysis, scientific research, and advanced internet search tools. 

How LLMs Enable a More Efficient Search Within Large Data Sets

One of the most exciting applications of large language models is in data search. Here are five capabilities that are speeding up the search process and improving results.

Advanced indexing

An index is a data structure used to organize data for search. Indexes contain information about the documents contained in a data set, such as keywords, topics, or embeddings that capture the semantic and contextual information of the data. Large language models can use indexes to process and analyze documents more efficiently. 

Deeper query understanding

Large language models can understand complex sentences and accurately gauge user intent. When a search query is submitted, the model interprets the query's meaning, making inferences based on syntax, semantics, and context. This enables users to quickly locate specific information within large data sets.

Superior search ranking

Large language models can be used to improve the search experience by more closely aligning search results with the intent of a user’s query. Search tools can harness the natural language capabilities of LLMs to produce more relevant and accurate results.

Contextual search

LLMs weigh the context of the search query or the user's previous interactions to provide a highly personalized, context-aware search experience. Contextual information, including user preferences, location, or browsing history, enables the model to tailor the search results to a specific user's needs and preferences.

Continuous learning and improvement

Large language models are dynamic tools that are continuously updated and refined as new data becomes available. In the process, search capabilities have adapted and improved over time with the addition of new information and a richer understanding of user preferences and search patterns.

Large Language Model Use Cases for Data Science

Large language models are used in numerous data science applications. Their ability to process and interpret vast amounts of text data have made them an indispensable part of many data science workflows. Here are four ways these models are being used to extract meaningful information.

Sentiment analysis

Sentiment analysis helps companies understand how customers feel about the quality of the products and services they provide, allowing them to respond to shifts in customer sentiment by adjusting product designs, the customer service experience, and a range of other factors impacting brand reputation. 

Large language models can conduct sentiment analysis, identifying and categorizing affective states and subjective information contained in text-based formats. LLMs are fine-tuned using a text data set with sentiment labels, enabling them to computationally identify and categorize opinions. 

Named Entity Recognition (NER)

A subcategory of natural language processing (NLP), named entity recognition (NER) is a method for detecting and categorizing name entities. Name entities are important pieces of information in unstructured textual data such as names, places, companies, and events. LLMs use deep learning algorithms that make them ideal for NER. They can readily adapt to the subtle nuances in written language, understand context, and generate logically consistent responses. NER is useful in many data science tasks, including entity extraction, data analysis, and product recommendation systems.

Text generation and summarization

Large language models are capable of generating high-quality and contextually relevant text. This technology can be used to develop chatbots that engage in conversational interactions with business users, helping them get accurate answers to their questions. LLMs are also well-suited to condensing large amounts of text into a more concise format, allowing them to quickly generate summaries of long documents.

Natural language understanding (NLU)

Written language is full of subtle connotations, intent, and emotion. Natural language understanding (NLU) is a branch of AI that attempts to decode the meaning embedded in human communication. Large language models are an important component of NLU and are used to improve natural language understanding tasks in data science. In combination with other technologies, large language models allow data scientists to extract subtle nuances in meaning from text data, such as product reviews, social media posts, and customer survey responses. 

Snowflake Brings AI-Enabled Search to the Data Cloud

Advances in artificial intelligence such as LLMs are accelerating the pace of innovation in data science, and Snowflake is bringing the power of generative AI to data search. Foundational to how businesses interact with data, conversational paradigms are rapidly changing how data scientists ask questions and retrieve information. Snowflake's acquisition of Neeva, a search company founded to make search even more intelligent at scale, is unlocking a unique and transformative search experience within the Data Cloud. With the advanced capabilities of generative AI-enabled searches, teams can quickly discover precisely the right data point, data asset, or data insight, making it possible to maximize the value of their data. 

In addition, Snowflake’s acquisition of Streamlit for building and sharing data apps and Applica for deep learning are adding even more advanced AI features to the Data Cloud.

Learn more: Using Snowflake and Generative AI to Rapidly Build Features