title: Tracking Universal Features Through Fine-Tuning and Model Merging
summary: We study how features emerge, disappear, and persist across models fine-tuned
  on different domains of text. More specifically, we start from a base one-layer
  Transformer language model that is trained on a combination of the BabyLM
  corpus, and a collection of Python code from The Stack. This base model is
  adapted to two new domains of text: TinyStories, and the Lua programming
  language, respectively; and then these two models are merged using these two
  models using spherical linear interpolation. Our exploration aims to provide
  deeper insights into the stability and transformation of features across
  typical transfer-learning scenarios using small-scale models and sparse
  auto-encoders.
paper_authors._id: 6710bd1963f0a6c38b3f9c53
paper_authors.name: Niels Horn
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-17T11:23:56.379Z
paper_authors._id: 63ca9515145e6c9716fbf376
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63ca9515145e6c9716fbf376/BXt9rJ5nIisijzFVLymai.png
paper_authors.fullname: Niels Horn
paper_authors.type: user
paper_authors.user: nilq
paper_authors._id: 6710bd1963f0a6c38b3f9c54
paper_authors.name: Desmond Elliott
paper_title: Tracking Universal Features Through Fine-Tuning and Model Merging
paper_summary: We study how features emerge, disappear, and persist across models fine-tuned
  on different domains of text. More specifically, we start from a base one-layer
  Transformer language model that is trained on a combination of the BabyLM
  corpus, and a collection of Python code from The Stack. This base model is
  adapted to two new domains of text: TinyStories, and the Lua programming
  language, respectively; and then these two models are merged using these two
  models using spherical linear interpolation. Our exploration aims to provide
  deeper insights into the stability and transformation of features across
  typical transfer-learning scenarios using small-scale models and sparse
  auto-encoders.
paper_ai_summary: Base Transformer models fine-tuned on different domains and merged using spherical linear interpolation reveal insights into feature stability and transformation in transfer learning.
paper_ai_keywords: Transformer language model spherical linear interpolation transfer-learning sparse auto-encoders