Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models
Uncovering how fine-tuning processes shift internal mechanics versus actual causal decision-making.
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, includin...
ArXiv CS.AI
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Understanding the discrepancy between internal state shifts and causal influence is crucial for improving the reliability and interpretability of fine-tuned models. This research provides a new lens to distinguish between cosmetic representational updates and functional changes that actually dictate model performance.
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