Learning to Interpret Language Model Diffs

Finetuning-induced changes to a model’s weights (a “model diff”) are semantically meaningful but often difficult to interpret. This makes us wonder: can we describe the content of an unknown model diff using natural language? We introduce diff interpretation training, a method that teaches a model describe its own finetuning-induced modifications. Our approach uses synthetic model diffs to train a lightweight adapter, which in turn can be applied to a compatible finetuned model to make it self-describing. Using two simple task settings, we demonstrate that our method can successfully decode model diffs into accurate natural language descriptions.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Goel, Avichal

Advisor dc:contributor.advisor

• Kim, Yoon

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/164839

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/164839