Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations
AuthorsHiskias Dingeto
Resources
The paper shows that fluent activation explanations can hide false claims and proposes training models so independent probes can catch those lies.
Key results
Centered reconstruction score despite weak claim-level grounding.
Approximate fraction of salient specific claims detected as reconstruction-dependent.
Standard-recipe runs developing co-adapted private codes.
Held-out loss increase in nats for effective 64-target supervision.
RECAP probe ranking true claims above false claims, versus 0.823 for control.
Approximate reconstruction-score penalty removed by score-optimizing report edits.
What the paper found
The paper, from StackOne Technologies, argues that reconstruction scores for natural-language activation explanations measure gist rather than claim-level faithfulness. On a released Qwen-2.5-7B verbalizer, explanations achieve a centered reconstruction score of 0.84, yet valid counterfactual flips find grounding in only about 2% of salient specific claims. In an exact synthetic sandbox, standard verbalizer–reconstructor training repeatedly creates co-adapted private codes: false wording becomes reconstruction-dependent in all 5 runs, so high reconstruction can reflect communication between the reader and writer rather than truthful description. The proposed repair, RECAP—Readable Encodings via Co-trained Auxiliary Predictors—trains the target model with external content targets and independently evaluated linear probes, instead of optimizing only the activation reader. RECAP preserves designated content at a language-modeling cost of +0.010 nats and transfers to Pythia-160M, where fresh probes distinguish true from false verbalizer claims with AUC 0.965, compared with 0.823 for the control. In the sandbox, RECAP yields claim-faithful explanations across all 5 runs, while the reconstruction score itself remains vulnerable: report-space adversaries suppress about 87% of the score penalty for lies, but RECAP probes continue detecting them. The authors also show that frozen probes become stale during continued training, whereas RECAP’s decodability must be maintained; importantly, probe-verifiable content is not necessarily fully verbalizable or behaviorally causal. The central safety lesson is to train models to retain independently checkable internal content, then use probes to verify explanations rather than trusting reconstruction quality.
Original abstract
Natural-language autoencoders score explanations of hidden activations by reconstruction: an explanation is deemed faithful if the activation can be regenerated from it. The test is structurally insensitive to individual false claims: if flipping a claim does not change the reconstruction, the claim is never penalized. We show the test is passed in two ways, neither faithful. On a released Qwen-2.5-7B verbalizer, explanations reconstruct well above chance while ~2% of specific claims are reconstruction-dependent, so the score tracks gist, not specific facts. Under exact synthetic ground truth, the standard recipe develops co-adapted private codes (false wording the reconstruction depends on) in 5/5 runs, and fixes that leave the target model unchanged do not help. We contribute two audit protocols, the grounded-vs-true cross and the evaluator swap, and RECAP (Readable Encodings via Co-trained Auxiliary Predictors): linear heads trained alongside the target model to keep designated content decodable. On RECAP-trained sandbox models, fresh verbalizers state the designated content truly and the codes vanish, at a +0.001-nat cost. This replicates on a pretrained Pythia-160M: the content becomes reliably probe-decodable, though a fresh verbalizer conveys it only in part (truth 0.44-0.46 vs a near-zero control). For interpretability, high reconstruction does not certify individual claims. For AI safety, RECAP makes designated internal content independently checkable against probes rather than asserted by prose a model can game: an independent probe scores the verbalizer's true claims above its false ones (AUC 0.96, vs 0.82 without RECAP). Against an adversary that edits an explanation to maximize the reconstruction score while lying (suppressing ~87% of its lie penalty), the RECAP probe still flags the lies (AUC 0.95) while the control probe collapses to chance (0.51).
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