Are Sparse Autoencoders Useful? A Case Study in Sparse Probing (2502.16681v1)

Abstract: Sparse autoencoders (SAEs) are a popular method for interpreting concepts represented in LLM activations. However, there is a lack of evidence regarding the validity of their interpretations due to the lack of a ground truth for the concepts used by an LLM, and a growing number of works have presented problems with current SAEs. One alternative source of evidence would be demonstrating that SAEs improve performance on downstream tasks beyond existing baselines. We test this by applying SAEs to the real-world task of LLM activation probing in four regimes: data scarcity, class imbalance, label noise, and covariate shift. Due to the difficulty of detecting concepts in these challenging settings, we hypothesize that SAEs' basis of interpretable, concept-level latents should provide a useful inductive bias. However, although SAEs occasionally perform better than baselines on individual datasets, we are unable to design ensemble methods combining SAEs with baselines that consistently outperform ensemble methods solely using baselines. Additionally, although SAEs initially appear promising for identifying spurious correlations, detecting poor dataset quality, and training multi-token probes, we are able to achieve similar results with simple non-SAE baselines as well. Though we cannot discount SAEs' utility on other tasks, our findings highlight the shortcomings of current SAEs and the need to rigorously evaluate interpretability methods on downstream tasks with strong baselines.

• The paper investigates the utility of sparse autoencoders for probing language model activations, finding they do not consistently outperform baseline methods like logistic regression across various challenging data regimes.
• Using a novel "Quiver of Arrows" metric across 113 datasets, the study found ensembles of baseline methods frequently match or outperform those enhanced with sparse autoencoders.
• Despite the theoretical interpretability advantage of sparse autoencoders, this study found it did not translate into practical performance improvements under rigorous empirical assessment.

Sparse Autoencoders: An Examination of Their Utility in Probing LLM Activations

The paper investigates the efficacy of Sparse Autoencoders (SAEs) in the context of probing LLM activations, specifically seeking evidence of their utility compared to more conventional probing methods. Despite the theoretical promise of sparse autoencoders — primarily their interpretability and selective activation patterns — empirical evaluations across various testing environments suggest that they do not consistently outperform baseline techniques. This paper provides a comprehensive look at the limitations and potential of SAEs, emphasizing the need for rigorous assessment of interpretability methods in machine learning.

Main Findings and Methodology

The authors set out to investigate SAEs by applying them to the task of LLM activation probing, an area where demonstrating improved performance over traditional methods could validate SAE utility. They interrogate the performance of these models across four challenging regimes: data scarcity, class imbalance, label noise, and covariate shift.

A key component of the paper is the use of a novel evaluation metric named "Quiver of Arrows," which involves assessing whether the addition of SAE probes to a probing toolkit yields better results than using established baselines alone. The results of this analysis, conducted across 113 diverse datasets, reveal that despite occasional successes on individual datasets, the ensemble methods composed entirely of baseline methods frequently match or outperform those enhanced with SAEs.

Additionally, probing techniques such as logistic regression, PCA regression, K-Nearest Neighbors, XGBoost, and multilayer perceptrons were tested against SAE-based techniques, focusing on outputs from LLM activation states. Layer 20 of the model was selected as a representative layer for most experiments, following a preliminary exploration of the model’s architecture.

Key Insights and Numerical Results

1. Performance in Challenging Regimes: The results demonstrate that SAE probes do not consistently exhibit an advantage in data scarcity, class imbalance, or label noise conditions. In fact, baseline methods, particularly logistic regression, remain competitive across most settings.
2. Covariate Shift Performance: Similarly, in environments involving covariate shifts, where robustness is paramount, conventional probes trained via logistic regression outmatched SAE-provided methods.
3. Interpretability: A notable theoretical strength of SAEs lies in their potential interpretability, facilitated by the generation of descriptive representations of activation features. However, this interpretability did not translate to practical performance improvements when assessed empirically.
4. Architectural Variations: Extensive variations in SAE architecture (including width and sparsity levels) demonstrate modest impacts on performance. Furthermore, a deeper analysis into various recently proposed SAE models suggests marginal improvements with newer designs, though not significantly beyond existing baselines.

Implications and Future Research Directions

The outcomes of this paper underscore a fundamental challenge in the field: while SAEs offer elegant theoretical constructs for interpretability, their practical utility under rigorous empirical scrutiny remains ambiguous. This calls for a reassessment of how SAEs are deployed in tasks requiring high reliability and precision.

Future research may benefit from focusing on configurations that integrate the interpretability strengths of SAEs without sacrificing performance. Also, further studies on models where the true latent structures are known could provide a clearer window into the potential capabilities of SAEs. Understanding the contexts and architectures that could leverage the interpretive bias of sparse autoencoders continues to be a fertile area of inquiry.

In conclusion, while SAEs provide intriguing possibilities for model interpretability, this paper positions them as currently secondary to baseline methods in probing LLM activations. This reflects a broader narrative in AI interpretability: elegant models must also meet rigorous practical performance standards to be deemed advantageous.