Large Language Models (LLMs) are incredibly versatile tools that can help data scientists across a broad spectrum of their work, from enhancing productivity and improving model performance to opening up new use cases. a962a0
1. Introducing your new assistant
The machine learning practice is about crafting modeling procedures to solve complex real-world problems. It requires a deep understanding of the challenges, engineering systems and technology, and solid statistical foundations to design the right solution.
But the practice also involves some tedious but necessary tasks to deliver value to end-users: writing training pipelines, performing exploratory analysis, reading articles and papers to stay informed about best practices and existing solutions.
LLMs can consume unstructured data, like documents and code, and structured data like tables. Because of this capability, they are a perfect fit for summarizing documents, generating analyses, and writing basic code blocks, enabling data scientists to focus on higher-value tasks.
2. Boost your model performance, reduce your development cycle
Data scientists used to solve natural language processing (NLP) tasks (sentiment analysis, information extraction) by training custom models from scratch. Because of their performance and ease of integration, LLMs have become competitive alternatives and are now the standard for many modeling tasks. Data scientists can use them off-the-shelf, finetune, or combine them with existing stacks.
LLMs have also led to step-change gains for search and recommendation applications, as vector databases and similarity search became core components of state-of-the-art systems.
3. Synthetic data generation / Cold-start problem
Cold start and data scarcity are foes of every data scientist. Modeling requires sufficient training data, but in many applications, data are simply unavailable, or at least insufficient, particularly at inception.
LLMs offer a new option to mitigate that issue. Because of their content generation ability, they can be used to generate synthetic data to bootstrap a modeling process. Data scientists can alternatively produce priors to model estimates.
4. Emerging opportunities
Lastly, LLMs have unlocked many previously unreachable use cases, solving problems beyond simple message completion and one-shot interaction.
For example, chatbots have been around for a while, but the technological limitations have often led to frustrating user experiences. LLMs now power applications that deliver high-quality interactions and leverage relevant knowledge bases and APIs to help answer user queries.
