Backpack Language Model

Backpack language models are a novel neural architecture designed to improve both the performance and interpretability of large language models. Research focuses on developing efficient methods for fine-tuning these models, such as sense finetuning, to achieve targeted improvements in areas like bias mitigation and knowledge enhancement, often outperforming traditional fine-tuning approaches. The architecture's key feature is its decomposition of word meanings into multiple "sense vectors," allowing for granular control and analysis of model behavior, leading to insights into how models generate text and enabling more targeted interventions. This approach holds promise for advancing both our understanding of language models and their practical applications, such as creating more controllable and less biased AI systems.