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Paper | Github | Demo Notebook

This post is about our recent paper Learning to Interpret Weight Differences in Language Models (Goel et al. Oct. 2025). We introduce a method for training a LoRA adapter that gives a finetuned model the ability to accurately describe the effects of its finetuning.

WeightDiffQA

Our paper introduces and attempts to solve a task we call WeightDiffQA[1]:

Given a language model _M_, a weight diff _delta_, and a natural language question _q_ about _delta_, output a correct natural language answer to _q_.

Here, a "weight [...]

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Outline:

(00:57) WeightDiffQA

(03:17) Diff Interpretation Tuning

(05:09) Eval #1: Reporting hidden behaviors

(07:17) Eval #2: Summarizing finetuned knowledge

(08:26) Limitations

(09:50) Takeaways

The original text contained 8 footnotes which were omitted from this narration.

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