MoRFI: Monotonic Sparse Autoencoder Feature Identification
Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of post-training often introduce new facts outwith the parametric knowledge, giving rise to hallucinations. While it has been demonstrated that supervised fine-tuning (SFT) on new knowledge may exacerbate the problem, the underlying mechanisms are still poorly understood. We conduct a controlled fine-tuning experiment, focusing on closed-book QA, and identify latent directions causally implicated in this degradation. Specifically, we fine-tune Llama 3.1 8B, Gemma 2 9B and Mistral 7B v03 on seven controlled mixtures of a single QA dataset, con...
| Authors | Dimitris Dimakopoulos, Shay B. Cohen, Ioannis Konstas |
|---|---|
| Primary category | cs.CL |
| Published | 29 April 2026 |
| Last updated at source | 17 August 2026 |
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