In the throes of World War II, amid the chaos and logistical obstacles of the battlefield, a unit achieved such extraordinary feats that it becomes a lasting legacy. 6888th Central Postal Catalog Battalion, known as “638” is an all-black women’s corps (WAC) This is the first time for units to be stationed overseas. Facing seemingly insurmountable challenges, they sorted through a backlog of millions of pieces of mail in record time, boosting soldier morale by reconnecting with family and loved ones.
Fast forward to today, and we have tools like OpenAI’s Large Language Models (LLM) that can parse complex data at scale. Imagine if this technology had existed during World War II. These powerful models can be fine-tuned to identify sender and receiver patterns, decipher illegible handwriting, and match incomplete addresses to military records. The LL.M. has advanced natural language processing (NLP) capabilities that simplify the once daunting task of ensuring accurate and efficient email distribution.
In this series, we explore how the pioneering work of 638 can be replicated and even enhanced by fine-tuning the LL.M. By delving into their heroic stories and showing how modern artificial intelligence can tackle similar challenges, we reveal the transformative potential of machine learning to solve real-world logistics problems—past, present, and future.