Researchers conduct detailed layer-by-layer analysis of how GPT-2 processes and interprets negative statements

Study performs causal analysis at both layer and attention head levels to understand negation handling in GPT-2

Research identifies specific neural mechanisms responsible for processing negative language in transformer models

Findings provide insights into interpretability of how large language models understand linguistic negation

Analysis contributes to better understanding of GPT-2's internal operations beyond black-box behavior