Synergy over Discrepancy: A Partition-Based Approach to Multi-Domain LLM Fine-Tuning

Neural Information Processing Systems 

Large language models (LLMs) demonstrate impressive generalization abilities, yet adapting them effectively across multiple heterogeneous domains remains challenging due to inter-domain interference. To overcome this challenge, we propose a partition-based multi-stage fine-tuning framework designed to exploit inter-domain synergies while minimizing negative transfer.