Every RAG-based localization pipeline has the same blind spot
The article discusses a common issue in retrieval augmented generation (RAG) localization pipelines, specifically the failure to accurately retrieve glossary terms. This problem arises from using sentence-level embeddings that overlook important phrase-level terminology. A proposed solution involves n-gram decomposition, which significantly improves retrieval accuracy for glossary terms in localization tasks.
- ▪Localization pipelines using RAG often miss applicable glossary terms due to retrieval recall issues.
- ▪The error in terminology is not visible unless someone is fluent in both languages and familiar with the glossary.
- ▪N-gram decomposition allows for better retrieval of glossary terms by treating phrases as independent queries.
Opening excerpt (first ~120 words) tap to expand
If a localization pipeline uses retrieval augmented generation to inject glossary terms into the model's context window, it has a retrieval recall problem that has never been measured.The pattern is universal: embed the input text, cosine-search a term bank, inject top-k results into the prompt. The output is grammatically correct. The terminology is wrong. The error is invisible unless someone speaks both languages and knows the glossary.We built this naive version first. Then we measured retrieval recall against production glossaries – and it turned out the system was missing the majority of applicable terms on real payloads.TechniqueRetrieval augmented localization (RAL) – context enrichment at inference timeCore fixN-gram decomposition before embedding, not sentence-level…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Lingo.dev.
Discussion
0 commentsMore from Lingo.dev
