Toolformer: Language Models Can Teach Themselves to Use Tools

Introduced self-supervised bootstrapping of API calls (calculators, search engines, translation), filtering out calls that fail to reduce perplexity on future tokens.

Directly guides architectural decisions, alignment strategy, and serving infrastructure for task-autonomous-agents, task-text-generation.

Assumptions

• Empirical distribution regularity holds and target domain adheres to pretraining linguistic/visual support

Limitations

• Resource scaling, inference memory requirements, and alignment robustness vary with model size and hardware topology