On the **MOOC-CS** prerequisite benchmark, graph-aware retrieval gains under the default **English-only** setup are **limited**, and become **substantial only after** introducing **language-matched query and encoder controls** (matching the language of the query and the encoder to the underlying course materials). The result is reported under the paper's strict-parity contract, which fixes the candidate pool, cutoff $$k$$, matching rule, and split policy and varies only the language-matching condition, so the change in graph gain is attributable to language matching rather than to graph policy. The finding illustrates that graph-specific gains can be masked or inflated by interface choices that are unrelated to graph structure.

MOOC-CS Graph Gain Requires Language-Matched Controls

This organizer keeps part 2 of the method details for Multilingual Encoder + CJK Query Rewrite as a MOOC-CS Control small enough for learners to study without a long flat child list.

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This organizer groups method, retrieval, ranking, and graph-structure details used by Multilingual Encoder + CJK Query Rewrite as a MOOC-CS Control.

Method Details for Multilingual Encoder + CJK Query Rewrite as a MOOC-CS Control (Auditable Strict-Parity Graph-RAG Paper)

Root URL: https://1iman.com/
Canonical paper URL: https://1iman.com/assets/working-papers/pdf/emnlp-auditable-strict-parity-evaluation-of-prerequisite-graph-retrieval-for-rag-under-leakage-controls.pdf
Portfolio PDF URL: https://1iman.com/assets/working-papers/pdf/emnlp-auditable-strict-parity-evaluation-of-prerequisite-graph-retrieval-for-rag-under-leakage-controls.pdf
Paper title: Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls
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Reference: Auditable Strict-Parity Evaluation of Prerequisite-Graph Retrieval for RAG under Leakage Controls

This organizer keeps part 1 of the method details for Multilingual Encoder + CJK Query Rewrite as a MOOC-CS Control small enough for learners to study without a long flat child list.

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