Paper Summary: Interpreting Language Model Parameters

{
  "doi": null,
  "year": null,
  "title": "Paper Summary: Interpreting Language Model Parameters",
  "authors": [],
  "abstract": "This paper introduces Adversarial Parameter Decomposition (VPD), a technique for interpreting language model parameters by decomposing weight matrices into simple, understandable rank-one components. By splitting the model's parameters into interpretable pieces, the authors can identify algorithms implemented in attention layers, edit model behavior without retraining, and recover small subnetworks responsible for specific behaviors. The approach represents a bottom-up form of interpretability that explains computation in the model's own terms rather than imposing external abstractions.",
  "arxiv_id": null,
  "pdf_status": "available",
  "openalex_id": null,
  "source_file": "paper.md",
  "ingested_via": "ingest_one_url",
  "fulltext_path": "/Users/antonborzov/Documents/Research.nosync/papers/paper.md"
}