Achieving robust generalization in speech deepfake detection (SDD) remains a primary challenge, as models often fail to detect unseen forgery methods. While research has focused on model-centric and algorithm-centric solutions, the impact of data composition is often underexplored. This paper proposes a data-centric approach, analyzing the SDD data landscape from two practical perspectives: constructing a single dataset and aggregating multiple datasets. To address the first perspective, we conduct a large-scale empirical study to characterize the data scaling laws for SDD, quantifying the impact of source and generator diversity. To address the second, we propose the Diversity-Optimized Sampling Strategy (DOSS), a principled framework for mixing heterogeneous data with two implementations: DOSS-Select (pruning) and DOSS-Weight (re-weighting). Our experiments show that DOSS-Select outperforms the naive aggregation baseline while using only 3% of the total available data. Furthermore, our final model, trained on a 12k-hour curated data pool using the optimal DOSS-Weight strategy, achieves state-of-the-art performance, outperforming large-scale baselines with greater data and model efficiency on both public benchmarks and a new challenge set of various commercial APIs.

Large Language Models (LLMs) show strong potential for reranking documents in information retrieval (IR), but training with monolingual data often leads to monolingual overfitting and lexical bias, limiting generalization in cross-lingual IR (CLIR). To overcome these issues, we investigate instruction-tuning LLaMA-3.1-8B-Instruct on English and multilingual code-switched data, and evaluate on mMARCO and XQuAD-R. Results show that instruction-tuning on code-switched data substantially improves CLIR performance, while monolingual tuning remains more effective for monolingual reranking. We introduce a novel measure to analyze the relationship between lexical overlap and reranking performance, showing that the two factors are correlated. We finally conduct a causal analysis using counterfactual examples, where we evaluate whether rewriting passages that share overlapping keywords with the query causes models to change their relevance predictions. Overall, we find that code-switching serves as an effective and lightweight strategy to improve cross-lingual generalization in LLM-based re-ranking, while our analyses show that lexical overlap remains a major factor that can mislead reranking models.