Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models

[Submitted on 17 Sep 2026 (v1), last revised 23 Sep 2026 (this version, v2)]

View PDF HTML (experimental)

Abstract:Fine-tuning has emerged as a widely adopted approach for adapting LLMs to a variety of downstream tasks. However, how it reshapes their internal mechanisms remains poorly understood. To address this, we investigate how fine-tuning alters internal representations in LLMs, including attention patterns and layer-wise activations, and examine whether these changes are linked to task-relevant components identified by EAP (e.g., attention heads and logit-level activations) that drive task performance. We find that EAP-identified components are concentrated within specific layers, indicating a degree of functional localisation in how models internalise task-specific behavior. Notably, the distribution of these components across layers is largely uncorrelated with the layers undergoing the most substantial representational changes during fine-tuning. Furthermore, we observe that overlap in EAP-identified components across tasks does not translate into cross-task performance transfer if the tasks are different in nature (e.g. classification vs. generative tasks). More specifically, fine-tuning on one task can lead to a degradation of performance on another when the two tasks exhibit a high degree of overlap in their EAP-identified components.

Submission history

From: Lingfang Li [view email]
[v1] Thu, 17 Sep 2026 21:55:20 UTC (1,793 KB)
[v2] Wed, 23 Sep 2026 08:52:42 UTC (1,733 KB)