· Xiaojing Yang · Model Evaluation
领域 MT 中的术语评估问题
为什么领域机器翻译必须单独评估术语，尤其是在自动指标可能掩盖关键技术错误的时候。
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Foundations and research notes on mathematics, statistics, machine learning, NLP, LLMs, model evaluation, and applied AI systems.
- · Xiaojing Yang · Multilingual AI
为什么 LoRA 适合低资源领域适配
从研究角度解释为什么 LoRA 适合低资源领域机器翻译：受控适配、更低实验成本，以及较低过拟合风险。
- · Xiaojing Yang · Explainability and Responsible AI
Ablation vs Shapley: Why Coalition Context Matters
Ablation is simple and useful, but Shapley values ask a broader question by averaging marginal contribution across many coalition contexts.
- · Xiaojing Yang · Explainability and Responsible AI
From Association to Causal Evidence: What Can Attribution Claim?
Training-data attribution can provide evidence of influence, but causal claims require careful interventions, retraining, controls, and uncertainty analysis.
- · Xiaojing Yang · Explainability and Responsible AI
Attribution Units: From Data Sources to Individual Examples
Why attribution units matter: source, group, document, example, and token-level attribution answer different research questions and support different kinds of evidence.
- · Xiaojing Yang · Explainability and Responsible AI
Coalitions, Marginal Contributions, and Shapley Values
The basic Shapley framework for data attribution: coalition value, marginal contribution, averaging over contexts, and why the result is more stable than one ablation.