Fine-Tuning Enhances Existing Mechanisms: A Case Study on Entity Tracking

Understand

Fine-tuning on generalized tasks such as instruction following, code generation, and mathematics has been shown to enhance language models' performance on a range of tasks.

• Nevertheless, explanations of how such fine-tuning influences the internal computations in these models remain elusive.
• We study how fine-tuning affects the internal mechanisms implemented in language models.
• As a case study, we explore the property of entity tracking, a crucial facet of language comprehension, where models fine-tuned on mathematics have substantial performance gains.