CausalGym: Benchmarking causal interpretability methods on linguistic tasks (2402.12560v1)

Abstract: LLMs (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.

• The paper presents a novel benchmarking suite that adapts SyntaxGym tasks to evaluate causal interpretability in language models.
• It empirically compares methods like DAS and linear probing, highlighting DAS for its superior alignment of neural representations with linguistic features.
• The study reveals that pythia models acquire complex syntactic phenomena in distinct stages, offering new insights into model behavior.

Benchmarking Causal Interpretability Methods on Linguistic Tasks

The paper under review introduces a novel approach aimed at bridging the gap between LLM (LM) interpretability and psycholinguistic research through a benchmarking suite designed to evaluate causal interpretative methods on linguistic tasks. This work provides a detailed analysis of how interpretability techniques, specifically targeting causal mechanisms, can influence the behavior of LMs when faced with linguistically driven tasks. The scope of the paper is entrenched in adapting and expanding the SyntaxGym suite to establish a robust groundwork for benchmarking interpretability methods capable of causally affecting model behavior.

The researchers conducted a focused paper on the pythia models, which range in complexity from 14 million to 6.9 billion parameters. They empirically investigate the causal efficacy of an array of interpretability methods, including linear probing and distributed alignment search (DAS). Notably, DAS demonstrably surpassed other methods, providing deeper insight into the learning trajectory for complex linguistic phenomena such as negative polarity item licensing and filler-gap dependencies. The findings reveal discrete stages in the learning mechanisms of LMs rather than a gradual progression, signaling important theoretical implications for understanding LM behavior.

Key Findings

1. Benchmark Suite Development: The paper details the adaptation of SyntaxGym tasks into , a suite designed for effectively benchmarking causal interpretability methods. This resource provides an extensive set of linguistically motivated scenarios that align with the causal variables sought in interpretability studies.
2. Methodological Evaluation: The comparison of interpretability methods, such as DAS, linear probing, and PCA, highlights DAS as a particularly efficacious approach for aligning neural representational features with causally effective linguistic features. This suggests that DAS is particularly suited for identifying neural regions and structures within LMs responsible for linguistic computations.
3. Learning Trajectories in LMs: Through dissecting tasks like negative polarity item licensing and filler-gap dependencies, the paper unveils a multi-step learning process within LMs. These results indicate that LMs synthesize complex syntactic dependencies in distinct phases rather than through linear accretion, offering compelling evidence of staged learning processes within neural networks.

Implications and Future Directions

The implications of this research are profound for advancing both theoretical and practical aspects of LM interpretability. From a practical standpoint, the suite serves as an invaluable tool for researchers seeking to validate and enhance interpretability methods across diverse linguistic contexts. Theoretically, it introduces a framework for understanding how LMs internalize and process complex syntactic relations, offering insights that could inform the development of more interpretable and effective neural architectures.

Looking forward, the pursuit of causal interpretability in LMs should extend beyond benchmarking performances to encompass additional linguistic and non-linguistic datasets, potentially across multiple languages, to fully harness the suite’s diagnostic capabilities. This work paves the way for future exploration into mechanistic interpretability, where understanding the exact neural firing and structural dependencies within networks remains a frontier of immense potential. The robustness of DAS, as evidenced in this paper, invites deeper inquiries into its broader applicability and optimization in other interpretability contexts.

In conclusion, this paper significantly contributes to the ongoing dialogue on model interpretability by showcasing a comprehensive and causally-grounded evaluation of interpretability methods via linguistically nuanced tasks. Providing both a benchmark and a methodological blueprint, this paper is poised to influence future research on the interpretability of complex models in AI.