Introduction

NLP Frontiers is a Stanford CS224N (Winter 2026) Final Project by Thibaud Clement.

This project explores how modern natural language processing systems can be made more efficient, selective, and computationally sustainable without sacrificing performance.

Motivated by the growing energy and hardware costs of large-scale language models, the project investigates mechanisms for reducing unnecessary computation—including selective information processing, retrieval efficiency, and budget-aware inference strategies.

The goal is to better understand how much information is truly required for strong performance, and to design principled methods for trading off accuracy, cost, and robustness in NLP systems.