Linear Separability of Activation Representations after Supervised Fine-Tuning on Incorrect Responses: A Study of Synthetic Dishonesty in Large Language Models
When a language model is fine-tuned to produce systematically incorrect responses, does this training leave a structured, linearly recoverable trace in its internal activations? We study this question in a controlled model-organism setting using five transformer architectures spanning 1.4 to 9 billion parameters. For each model, an "honest" and a "dishonest" LoRA fine-tuned variant are constructed using identical question distributions but correct versus plausible-but-incorrect answers. Linear probes reach near-ceiling separability (AUC >= 0.9997) within the first few layers in four of five architectures. This separability transfers from TruthfulQA to held-out MMLU subjects for four model...
| Authors | Vahideh Zolfaghari |
|---|---|
| Primary category | cs.LG |
| Published | 28 May 2026 |
| Last updated at source | 5 September 2026 |
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