From documents, chunks, and embeddings to retrieval, reranking, evidence completeness, augmentation, and generation: a complete visual foundation for RAG.
A research note on how to study the effect of domain data size in LoRA-based machine translation adaptation.
A research-story version of my LoRA NMT project: problem framing, data, method, evaluation, findings, limitations, and future work.
Why terminology should be evaluated explicitly in domain machine translation, especially when automatic metrics can hide critical technical errors.
A research-facing explanation of why LoRA is a good fit for low-resource domain machine translation: controlled adaptation, lower experimental cost, and reduced overfitting risk.
Ablation is simple and useful, but Shapley values ask a broader question by averaging marginal contribution across many coalition contexts.