Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe
Abstract
Probing is widely used to study which features can be decoded from language model representations. However, the common decoding probe approach has two limitations that we aim to solve with our new encoding probe approach: contributions of different features to model representations cannot be directly compared, and feature correlations can affect probing results. We present an Encoding Probe that reverses this direction and reconstructs internal representations of models using interpretable features. We evaluate this method on text and speech transformer models, using feature sets spanning acoustics, phonetics, syntax, lexicon, and speaker identity. Our results suggest that speaker-related effects vary strongly across different training objectives and datasets, while syntactic and lexical features contribute independently to reconstruction. These results show that the Encoding Probe provides a complementary perspective on interpreting model representations beyond decodability.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of the Encoding Probe, a novel method for reconstructing language model representations that allows for direct comparison of feature contributions while controlling for correlations. This work significantly advances the interpretability of language models and provides a framework for future research in this area.
Methodology
The proposed Encoding Probe method innovatively reverses the traditional probing direction, allowing for a more nuanced understanding of feature contributions in language model representations. By employing regression analysis to reconstruct model representations based on interpretable features, the methodology effectively addresses the limitations of conventional decoding probes. The use of multivariate ridge regression for feature ablation is a robust choice, though the reliance on linear models may limit the exploration of potential nonlinear relationships.
Results & evaluation
The experiments are well-structured, focusing on two case studies that highlight the Encoding Probe's capabilities in analyzing speaker identity and syntactic features across different transformer models. The results convincingly demonstrate the method's ability to quantify feature contributions while controlling for correlations, providing insights that traditional decoding probes cannot achieve. The thorough evaluation across various models adds to the robustness of the findings.
Reproducibility
The paper provides sufficient details regarding the implementation of the Encoding Probe, including the datasets used and the regression techniques applied. However, the absence of publicly available code or data limits the reproducibility of the experiments. Clear documentation of the experimental setup and results would enhance reproducibility.
Limitations
The Encoding Probe, while addressing some probing limitations, remains observational and cannot establish causal relationships. The choice of features for analysis may also introduce biases, and the focus on read audiobook data may not generalize to spontaneous speech scenarios. Additionally, the linear regression approach may overlook complex interactions in the data.
Broader impact
This work has significant implications for the interpretability of language models, particularly in understanding how various linguistic features are encoded. The Encoding Probe could influence future research in model interpretability and lead to improved designs for language models that prioritize feature disentanglement. The findings may also inform ethical considerations regarding the use of language models in sensitive applications. The main contribution of this paper is the introduction of the Encoding Probe, a novel method for reconstructing language model representations that allows for direct comparison of feature contributions while controlling for correlations. This work significantly advances the interpretability of language models and provides a framework for future research in this area.
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