Differential syntactic and semantic encoding in LLMs

1. 1Build a dataset of sentences with varying syntactic structures and meanings
2. 2Run experiments to average hidden-representation vectors of sentences sharing syntactic structure or meaning
3. 3Configure a model to subtract these averaged vectors from the original representations
4. 4Test the resulting vectors for their ability to capture syntactic and semantic information
5. 5Apply the findings to improve the performance of LLMs on natural language processing tasks

NLP researchers and AI engineers on a team can benefit from understanding how LLMs represent syntactic and semantic information, as it can inform the development of more effective language models and improve their performance on downstream tasks

💡 LLMs encode syntactic and semantic information in their inner layers in a way that can be captured by averaging and subtracting hidden-representation vectors

🤖 New study reveals how LLMs encode syntax & semantics in their inner layers! 📊