Living off the land: Solving ML problems without training a single model

Introduction

The concept of living off the land is related to surviving on what you can forage, hunt, or grow in nature.

Considering the current Machine Learning landscape, we can draw a parallelism between living off the land and "shopping around" for ready-made models for a given task.  While this has been partially true for some time thanks to model repositories such as HuggingFace, it still required some degree of involvement by applying finetuning or retraining for most advanced use cases. 

However, the appearance of large language models (LLMs) with instruction-following capabilities beyond next-word prediction has opened the doors to many applications that require little supervision, and in some cases, true 100% no-code solutions.

In this post I will be describing a recent "living off the land" approach in order to solve an NLP competitive ML challenge: WASSA 2023, An ACL shared Task on Empathy Emotion and Personality Detection in Interactions