The rapid advancement of Multi-modal Language Models (MLLMs) has significantly enhanced performance in multimodal tasks, yet these models often exhibit inherent biases that compromise their reliability and fairness. Traditional debiasing methods face a trade-off between the need for extensive labeled datasets and high computational costs. Model merging, which efficiently combines multiple models into a single one, offers a promising alternative but its usage is limited to MLLMs with the same architecture. We propose 3DM, a novel framework integrating Distill, Dynamic Drop, and Merge to address these challenges. 3DM employs knowledge distillation to harmonize models with divergent architectures and introduces a dynamic dropping strategy that assigns parameter-specific drop rates based on their contributions to bias and overall performance. This approach preserves critical weights while mitigating biases, as validated on the MMSD2.0 sarcasm detection dataset. Our key contributions include architecture-agnostic merging, dynamic dropping, and the introduction of the Bias Ratio (BR) metric for systematic bias assessment. Empirical results demonstrate that 3DM outperforms existing methods in balancing debiasing and enhancing the overall performance, offering a practical and scalable solution for deploying fair and efficient MLLMs in real-world applications.

In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demonstrations. In this work, we introduce syntactic knowledge to select better in-context examples for machine translation (MT). We propose a new strategy, namely Syntax-augmented COverage-based In-context example selection (SCOI), leveraging the deep syntactic structure beyond conventional word matching. Specifically, we measure the set-level syntactic coverage by computing the coverage of polynomial terms with the help of a simplified tree-to-polynomial algorithm, and lexical coverage using word overlap. Furthermore, we devise an alternate selection approach to combine both coverage measures, taking advantage of syntactic and lexical information. We conduct experiments with two multi-lingual LLMs on six translation directions. Empirical results show that our proposed SCOI obtains the highest average COMET score among all learning-free methods, indicating that combining syntactic and lexical coverage successfully helps to select better in-context examples for MT. Our code is available at https://github.com/JamyDon/SCOI.