FINE-TUNING ENHANCES EXISTING MECHANISMS: A CASE STUDY ON ENTITY TRACKING

FINE-TUNING ENHANCES EXISTING MECHANISMS: A CASE STUDY ON ENTITY TRACKING

22 Feb 2024 | Nikhil Prakash1*, Tamar Rott Shaham2, Tal Haklay3, Yonatan Belinkov3, David Bau1
The paper investigates how fine-tuning on generalized tasks, such as mathematics, enhances the performance of language models on entity tracking, a crucial aspect of language comprehension. The authors explore the internal mechanisms of entity tracking in language models, focusing on the LLaMA-7B model and its fine-tuned variants. They identify and analyze the entity-tracking circuit in the original model and its fine-tuned versions, finding that the same circuit is present in both. The performance boost in fine-tuned models is attributed to their improved ability to handle augmented positional information. The study uses Patch Patching and Desiderata-based Component Masking (DCM) to uncover the mechanisms underlying the enhanced performance. The findings suggest that fine-tuning primarily enhances existing mechanisms rather than introducing fundamental shifts, providing valuable insights into how models execute tasks. The research highlights the importance of understanding the underlying mechanisms to promote beneficial applications of AI systems.The paper investigates how fine-tuning on generalized tasks, such as mathematics, enhances the performance of language models on entity tracking, a crucial aspect of language comprehension. The authors explore the internal mechanisms of entity tracking in language models, focusing on the LLaMA-7B model and its fine-tuned variants. They identify and analyze the entity-tracking circuit in the original model and its fine-tuned versions, finding that the same circuit is present in both. The performance boost in fine-tuned models is attributed to their improved ability to handle augmented positional information. The study uses Patch Patching and Desiderata-based Component Masking (DCM) to uncover the mechanisms underlying the enhanced performance. The findings suggest that fine-tuning primarily enhances existing mechanisms rather than introducing fundamental shifts, providing valuable insights into how models execute tasks. The research highlights the importance of understanding the underlying mechanisms to promote beneficial applications of AI systems.
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