Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe
Researchers introduce an encoding probe that flips the conventional interpretability paradigm by reconstructing model internals from linguistic features rather than decoding features from representations. This addresses a fundamental limitation in probing methodology: the inability to directly compare feature contributions and the confounding effects of correlations. Testing across text and speech transformers reveals that speaker identity effects vary significantly by training objective and dataset, while syntactic and lexical patterns show more consistency. The work matters because it provides a more rigorous foundation for understanding what language models actually encode, moving beyond surface-level feature detection toward causal attribution of learned representations.
Modelwire context
ExplainerThe encoding probe inverts the causal direction of probing entirely. Instead of asking 'what linguistic features can we extract from a model's hidden states,' it asks 'given linguistic features, how well can we reconstruct the actual representations?' This distinction matters because conventional decoding probes conflate correlation with causation, masking which features the model actually depends on versus which merely correlate with its outputs.
This work sits in a lineage of papers pushing interpretability beyond surface-level feature detection. The Aitchison embeddings paper from early May tackled compositional interpretability in graphs by exposing archetypal roles; this encoding probe does similar work for transformers by forcing a reconstruction bottleneck that reveals what representations actually encode. Both papers share a core insight: opaque vector spaces become legible only when you constrain what information can flow through them. The local attention expressivity paper from the same period also formalized why intuitive efficiency tradeoffs sometimes fail, suggesting the field is maturing toward mechanistic explanations rather than empirical pattern-matching.
If the encoding probe methodology produces different causal rankings of features than standard decoding probes on the same model checkpoints, and if those rankings correlate with ablation studies (removing top-ranked features and measuring performance drop), then this becomes a standard tool. If the rankings don't predict ablation outcomes better than conventional probes, the inversion is elegant but not actionable.
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MentionsEncoding Probe · transformer models · language models · speech transformers
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