Fine-tuning reshapes LLM representations without aligning causal components, study says
Fine-tuning is a common method for adapting large language models (LLMs) to new tasks, yet its impact on internal mechanisms is not well understood. The authors examine changes in attention patterns and layer‑wise activations after fine‑tuning, and compare those changes to task‑relevant components identified by the Explainable AI Probes (EAP) framework, such as specific attention heads and…
Key points
- EAP‑identified components cluster in specific layers, showing functional localisation of task behavior.
- Layers with largest representational changes during fine‑tuning are largely uncorrelated with EAP component layers.
- Overlap of EAP components across tasks does not ensure transfer and can degrade performance on other tasks.
They report that EAP‑identified components tend to concentrate in particular layers, indicating functional localisation of task‑specific behavior. However, the layers showing the greatest representational shifts during fine‑tuning are largely uncorrelated with the layers that host these causal components. Moreover, overlapping EAP components across different tasks does not guarantee performance transfer; when tasks differ (e.g., classification versus generative), overlap can even cause degradation of performance on the secondary task.
The findings suggest a disconnect between where fine‑tuning alters internal representations and which components actually drive task performance, highlighting potential pitfalls for transfer learning and multi‑task adaptation.
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