Papers › CausalGym: Benchmarking causal interpretability methods on linguistic tasks
CausalGym: Benchmarking causal interpretability methods on linguistic tasks
Aryaman Arora, Dan Jurafsky, Christopher Potts
Language models (LMs) have proven to be powerful tools for psycholinguistic research, but most prior work has focused on purely behavioural measures (e.g., surprisal comparisons). At the same time, research in model interpretability has begun to illuminate the abstract causal mechanisms shaping LM behavior. To help bring these strands of research closer together, we introduce CausalGym. We adapt and expand the SyntaxGym suite of tasks to benchmark the ability of interpretability methods to causally affect model behaviour. To illustrate how CausalGym can be used, we study the pythia models (14M--6.9B) and assess the causal efficacy of a wide range of interpretability methods, including linear probing and distributed alignment search (DAS). We find that DAS outperforms the other methods, and so we use it to study the learning trajectory of two difficult linguistic phenomena in pythia-1b: negative polarity item licensing and filler--gap dependencies. Our analysis shows that the mechanism implementing both of these tasks is learned in discrete stages, not gradually.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Interpretability Techniques for Deep Learning | CausalGym | DAS | Log odds-ratio (pythia-6.9b) | 9.95 | #1 of 7 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CausalGym | Linear probe | Log odds-ratio (pythia-6.9b) | 3.42 | #2 of 7 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CausalGym | Difference-in-means | Log odds-ratio (pythia-6.9b) | 2.91 | #3 of 7 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CausalGym | k-means | Log odds-ratio (pythia-6.9b) | 1.87 | #4 of 7 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CausalGym | PCA | Log odds-ratio (pythia-6.9b) | 1.81 | #5 of 7 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CausalGym | LDA | Log odds-ratio (pythia-6.9b) | 0.27 | #6 of 7 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CausalGym | Random | Log odds-ratio (pythia-6.9b) | 0.01 | #7 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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