CC-Tuning: A Cross-Lingual Connection Mechanism for Improving Joint Multilingual Supervised Fine-Tuning

Current large language models (LLMs) often exhibit imbalanced multilingual capabilities due to their English-centric training corpora. To address this, existing fine-tuning approaches operating at the data-level (e.g., through data augmentation or distillation) typically introduce implicit cross-lingual alignment, overlooking the potential for more profound, latentlevel 1 cross-lingual interactions. In this work, we propose CC-TUNING, a novel multilingual fine-tuning paradigm that explicitly establishes a cross-lingual connection mechanism at the latent level. During training, CC-TUNING fuses the feed forward activations from both English and non-English inputs, enabling the model to benefit from both linguistic resources. This process is facilitated with a trainable Decision Maker that identifies beneficial activations. Furthermore, during inference, a Transform Matrix is utilized to simulate the cross-lingual connection under monolingual setting through representation transformation. Our experiments on six benchmarks covering 22 languages show that CC-TUNING outperforms vanilla SFT and offers a strong latent-level alternative to datalevel augmentation methods. Further analysis also highlights the practicality of CC-TUNING and the potential of latent-level cross-lingual interactions in advancing the multilingual performance of LLMs. (Code link: CC-Tuning) * Corresponding Author 1 latent-level: referring to direct manipulation of the model's internal representations (e.g., FFN activations) Vanilla Supervised Fine-Tuning Multilingual Supervised Training Data (Pairs)