Why this matters Most multilingual retrieval pipelines treat semantic relevance and document language as independent signals, which can produce answers in a different language than the user query—problematic for downstream generation in RAG systems. LAMAR focuses on the often-overlooked objective of language coherence: given semantically equivalent documents across languages, a reranker should prefer the one in the query language without sacrificing relevance.
Key Findings
- English-anchored relevance distillation: uses English-grounded scores to make relevance assessments consistent across multilingual inputs, reducing cross-language scoring drift.
- Preference alignment for language coherence: learns to tilt rankings toward documents that match the query language while retaining semantic relevance, producing more language-consistent top results.
- Empirical performance: in a controlled language-coherence experiment LAMAR achieves the best overall and per-language results; on established multilingual reranking benchmarks it remains competitive, and in practical first-stage retrieval settings it improves end-to-end retrieval metrics when used to rerank candidates.
Who it's for and trade-offs
Great fit if you build multilingual RAG or search systems where answer language matters (user-facing QA, multilingual assistants, cross-lingual search) and you need a reranker that enforces language coherence without major drops in relevance. Look elsewhere if your pipeline requires purely language-agnostic aggregation (e.g., cross-lingual pooling where language diversity is preferred) or if latency/compute constraints cannot accommodate a cross-encoder reranker.
Method sketch
The model is a multilingual cross-encoder trained to combine semantic relevance and language-preference signals. Training uses an English-anchored teacher to harmonize relevance scores across languages, plus an alignment objective that biases rankings toward query-language documents. The paper evaluates both controlled language-coherence settings and standard multilingual reranking benchmarks to show the trade-offs between coherence and raw relevance.