# Ravel Eval

> Evaluates interpretability methods' ability to disentangle polysemantic language model representations by isolating causal attributes through activation interventions on residual stream features. Use when the user wants to benchmark on RAVEL, or asks about evaluating this task. Reports Disentanglescore.

- Skill: `qhjqhj00/ravel-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/ravel-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/ravel-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/ravel-eval

---


# ravel-eval

> RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations — Huang et al. (2024) (arXiv:2402.17700, 2024)

## What this evaluates

Evaluates interpretability methods' ability to disentangle polysemantic language model representations by isolating causal attributes through activation interventions on residual stream features.

## Datasets

- **RAVEL** — total ?; splits: test (-1), Entity (-1), Context (-1); repo https://github.com/explanare/ravel

## Metrics

- `Disentanglescore` **(primary)** — range: [0, 1]
  - Composite metric combining causal attribution and isolation scores to measure how well a learned feature subspace isolates a target attribute from others.
- `Causescore` — range: percent
  - Measures the causal effect of intervening on a target feature subspace on a specific attribute. Defined as the percentage of times intervening on one attribute's feature changes another attribute.
- `Iso` — range: [0, 1]
  - Measures the isolation of a learned feature, indicating how independent it is from other attributes during intervention.

## Input / output format

**Input**: Residual stream representation vectors N^L_tE at the last token of an entity across Transformer layers L in {1, ..., 32}.

**Output**: A targeted feature subspace F_A (or intervention vector) intended to isolate a specific attribute from the representation.

## Scoring recipe

```python
# For each layer L and entity representation N^L_tE:
# 1. Extract learned feature subspace F_A for target attribute.
# 2. Perform interchange intervention: replace N^L_tE with F_A.
# 3. Compute Cause score: measure causal effect on target attribute.
# 4. Compute Iso score: measure independence from other attributes.
# 5. Compute Disentanglescore = f(Cause, Iso).
# Return max Disentanglescore across all layers on test split.
```

## Common pitfalls

- Methods that do not use counterfactual supervision (e.g., PCA, Sparse Autoencoder) struggle significantly compared to intervention-trained methods.
- Some attribute pairs (e.g., country-language, latitude-longitude) are inherently difficult to disentangle due to unavoidable ripple effects, regardless of the method used.
- Evaluations are limited to the last token of the entity in the residual stream; ignoring earlier tokens or other representation sites may miss important disentanglement signals.

## Evidence (verbatim from paper)

> We evaluate each method on every representation N^L_tE and report the highest disentanglement score on test splits in Table[2]. A Causescore of 0.62 for column Continent, row Timezone (bottom left corner), means that, when intervening on the Continent feature, the same subspace changes Timezone 62% of the time.

## Citation

```bibtex
@misc{huang2024ravel,
  title={RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations},
  author={Huang et al. (2024)},
  year={2024},
  note={arXiv:2402.17700}
}
```

- arXiv: 2402.17700