Reproduce “Language models struggle with compartmentalization” on OpenResearch

About this paper

Thomas Vincent Howe, David Wingate

Researchers from Brigham Young University define and systematically investigate "compartmentalization" in language models, where distinct presentations of the same underlying data are processed as separate internal representations. The study demonstrates this leads to sample inefficiency and capacity competition but also shows that unified solutions are achievable through specific initializations or fine-tuning, providing a framework to understand LLM limitations in knowledge generalization and contextual recall.

Key insights

• Compartmentalization, which leads to sample inefficiency and capacity competition, is not an inherent architectural limitation but rather an optimization challenge, as unified solutions are attainable through specific initializations or fine-tuning.
• The effectiveness of interventions like paired "translation" data and contrastive alignment exhibits complex, c-dependent phase transitions, indicating that representation unification depends on a subtle interplay of model scale, data characteristics, and training methodologies.
• In certain scenarios, such as when processing different formats of the same underlying data, compartmentalization can inadvertently prevent destructive interference between competing representations, albeit at the cost of unified knowledge.

Summary by alphaXiv.