aiqbench analysis
This research introduces SAEExplainer, a novel framework that employs activation scores as reward signals to iteratively refine the interpretability of Sparse Autoencoders in large language models. By shifting from an open-loop to a feedback-driven optimization process, the method significantly reduces explanation hallucinations and strengthens causal triggering patterns. This advancement directly addresses the central challenge of making LLM internal representations more transparent and reliable for analysis.
SAEExplainer: Interpreting SAE Features with Activation-Guided Preference Optimization
arXiv:2606.08496v2 Announce Type: replace Abstract: Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge. Current explanation methods, however, typically operate within an open-loop paradigm, failing to leverage mechanistic feedback for further refinement. In this paper, we propose SAEExplainer, a training framework that utilizes activation scores as an objective reward signal to train the model for self-correction and iterative bootstrapping. By iteratively verifying and correcting foundational explanations through a two-round optimization process, SAEExplainer achieves continuous improvement in its explanatory capabilities. This mechanism significantly reduces explanation hallucinations and reinforces causal triggering patterns. Extensive experiments demonstrate our approach improves upon established baselines across most metrics, especially in causal triggering and discriminative activation. The code is available at https://github.com/he-jingyi/SAEExplainer.
Abstract:Although Sparse Autoencoders (SAEs) have mitigated the opacity of large language models (LLMs) by decomposing dense representations into sparse features, explaining these features still remains a central challenge. Current explanation methods, however, typically operate within an open-loop paradigm, failing to leverage mechanistic feedback for further refinement. In this paper, we propose SAEExplainer, a training framework that utilizes activation scores as an objective reward signal to train the model for self-correction and iterative bootstrapping. By iteratively verifying and correcting foundational explanations through a two-round optimization process, SAEExplainer achieves continuous improvement in its explanatory capabilities. This mechanism significantly reduces explanation hallucinations and reinforces causal triggering patterns. Extensive experiments demonstrate our approach improves upon established baselines across most metrics, especially in causal triggering and discriminative activation. The code is available at this https URL.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2606.08496 [cs.CL]
(or arXiv:2606.08496v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.08496 Focus to learn more arXiv-issued DOI via DataCite
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