[Proposal] Demo or helper for statistical audits of activation-patching results on TransformerBridge

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
28/100
Tipo de issue
Nueva funcionalidad
Claridad
Necesita aclaración
Estado de actividad
Activo
Stack tecnológico
python
Área
machine-learning

Línea de trabajo

Start by reading the proposal’s discussion and the current TransformerBridge analysis and notebook interfaces. The issue leaves the deliverable undecided between a demo notebook, a helper package, or no repository addition, so first establish maintainer agreement on scope and delivery format. Done means the accepted path is clear and its statistical assumptions and multiple testing approach are documented.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

complexity-moderate enhancement TransformerBridge
Proposal

Add a focused demo or analysis example showing how to use a statistical audit layer for activation-patching results in the current TransformerBridge API, without turning that functionality into a core library method.

Motivation

The current TransformerLens v4 stack is built around TransformerBridge and the newer analysis/tooling interfaces, not the older HookedTransformer API. A prior LAT-style implementation or similar analysis utility may have been natural in v3, but it needs to be rewritten or re-scoped to work with the current bridge-based model interface.

A statistical audit layer is valuable when evaluating many components at once (heads, layers, positions, or intervention sites), especially when the analysis asks whether the observed patching signal survives multiple-comparisons correction. That is a natural fit for a research demo or notebook example, rather than necessarily a first-class core method in TransformerLens itself.

Scope

We should decide whether the repository wants:

  1. a small demo notebook showing statistical audits on real TransformerBridge runs, or
  2. a generic analysis utility in a separate package / helper module, or
  3. no direct repository addition yet, pending the broader analysis-tool overhaul.
Proposed direction

A good low-friction path would be:

  • keep statistical inference in a standalone or external helper package if needed,
  • adapt it to TransformerBridge rather than HookedTransformer,
  • present it as a notebook or docs example to make the methodology concrete,
  • keep the core library focused on generalized analysis primitives rather than a single lat_scan-like API.
Concrete example

A notebook could demonstrate:

  • running a clean/corrupt activation-patching experiment with TransformerBridge,
  • measuring per-component normalized effects,
  • reporting descriptive rankings and robustness diagnostics,
  • applying a valid multiple-testing procedure only when a defensible null is available,
  • documenting which assumptions are required for sign-flip or control-based inference.

The goal would be to make the statistical audit methodology usable and inspectable without hard-coding one specific method into the repository core.

Discussion points
  • Should TransformerLens accept a demo notebook for statistical analysis tools in the current v4 architecture?
  • Should generic statistical audits live in a dedicated package or helper module instead of the core repo?
  • If a core API is eventually added, should it be a generic analysis primitive instead of a single lat_scan-like method?
  • What is the preferred maintenance boundary for visualization-heavy or method-specific research tooling?
Checklist
  • I have checked that there is no similar issue or proposal in the repository.
  • Maintainers agree on the preferred scope and delivery format.
  • The proposal is rewritten for the TransformerBridge v4 API.
  • The accepted path is either a demo notebook or a generic helper layer, rather than a single bespoke method.
Lenguaje dominante
Python
Estrellas
3.9k
Forks
708
Merge medio
1 d 9 h
PR fusionados (30 d)
84

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Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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