Are LLMs Only Good for Chat-Based Solutions? Exploring Beyond Language Tasks

Author(s): Andy Spezzatti

Originally published on Towards AI.

Beyond Words: LLMs Enhance Data Analysis from Genomics to Strategy
Source: Image by Nicole Herero on Unsplash

Over the past two years, Large Language Models (LLMs) including ChatGPT, Antropic, and Mistral have transformed our engagement with technology. These models have become central across a range of fields, from boosting productivity to fostering innovation in sectors not traditionally linked with language processing. This analysis examines the contributions of LLMs to diverse areas such as time series prediction, genomics, recommender systems, and strategic scenario planning, showcasing their versatility and innovative potential.

“The inherently contextual nature of words and sentences is at the heart of how LLMs work.” Yann LeCun

Time series forecasting has traditionally relied on models like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, which are designed explicitly for sequential data. But the recent widespread success of LLMs suggests their potential applicability and performances in adjacent fields, such as time series forecasting, which was explored in a recent study, Chronos.

Chronos is based on the observation that both language sequences and time series share a fundamental goal: to predict future elements based on preceding sequences. This led to the creation of a framework that adapts LLMs to time series without extensive changes to the models’ architecture. It does this by converting time… Read the full blog for free on Medium.

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Published via Towards AI