- The paper demonstrates that Sparse Autoencoders can extract interpretable latent features from synthetic formal languages, though these features lack clear causal impact.
- The study rigorously tests L1 and top-k regularization methods to reveal how variations in inductive biases affect feature disentanglement.
- The authors propose integrating causal constraints during training to enhance the reliability of disentangled representations in NLP models.
Analyzing (In)Abilities of Sparse Autoencoders via Formal Languages
The paper under discussion examines the use of Sparse Autoencoders (SAEs) for interpretable and disentangled feature learning within the domain of formal languages. The focus is on assessing both the potential and limitations of SAEs in disentangling hidden representations of LLMs, thereby addressing a gap in the literature where these methods have predominantly been evaluated in the context of visual data.
Overview of Study and Methods
The authors explore the use of SAEs on transformer models trained on synthetic formal languages, specifically Dyck-2, Expr, and a subset of English Probabilistic Context-Free Grammars (PCFG). These synthetic environments provide a controlled setting to investigate how SAEs can learn latent features that are semantically interpretable and whether these features have any causal impact on the model’s computation.
A broad range of hyperparameter settings are employed, including variations in sparsity regularization and model normalization procedures. The paper leverages two main sparsity-imposing strategies: L1 regularization and top-k activation regularization. These strategies help to enforce sparsity by limiting the number of active neurons in the hidden layer of the autoencoder, which theoretically should enhance interpretability.
Key Findings
The paper reports several intriguing findings:
- Interpretable Features: When SAEs are applied to these formally structured languages, interpretable latent features surprisingly often emerge. Examples include the emergence of features corresponding to grammatical constructs like parts of speech in the English grammar.
- Sensitivity to Inductive Biases: Performance of the SAEs is highly sensitive to inductive biases. For instance, variations in hyperparameter settings or regularization methods (like L1 versus top-k) significantly influence not only the quality of learned features but also whether these features can be reliably identified at all.
- Lack of Causal Relevance: Despite identifying latents correlating with syntactic or semantic features of the languages, the paper demonstrates these do not necessarily imply causal relevance. Interventions on the learned latent features often do not yield expected modifications in model behavior, calling into question the causal validity of disentangled representations in this context.
Proposed Method for Causal Feature Learning
Responding to the discovery that interpretability does not always equate to causal functionality, the authors advocate for an approach that incorporates causal considerations into the training pipeline. They propose employing token-level correlations as weak supervision signals during training. By imposing constraints that emphasize causally relevant features, the paper suggests a pathway toward achieving more reliable disentanglement.
Implications and Speculations
The implications of this work touch on both theoretical and practical arenas. Theoretically, it challenges the assumption that disentangled features necessarily enhance understanding of model computations. Practically, it points toward the considerable care needed in designing SAE approaches for interpretability in NLP applications. The paper sets a foundation for further exploration on how to integrate causality into feature identification effectively.
Future investigations could refine these preliminary results and explore new methodologies that more explicitly couple causality with feature disentanglement. This might include leveraging more sophisticated causal inference methods or adapting regularization techniques to better capture intricate causal dynamics in neural computations.
The findings underscore the importance of synthetic benchmarks, which provide clarity on fundamental issues and allow controlled variations, to assess model interpretability challenges. This work adds to the growing literature emphasizing the complexity and nuances involved in interpreting representations in modern LMs, suggesting that achieving genuine interpretability may require innovative methodological advances.