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arXiv — cs.AI

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标题为英语Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models

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,…

arXiv — cs.AI作者: Lingfang Li, Procheta Sen, Shubham Das, Danushka Bollegala2026年9月22日 (9月22日)
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  • 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,…
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Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models

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