Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion

This project is a very recent research-oriented implementation (4 days old) associated with a paper tackling a specific failure mode in multilingual RAG: the tendency of models to favor English even when local language context is sufficient. The core insight—that 'exposure bias' and 'gold availability' in benchmarks distort our understanding of LLM multilingual capabilities—is academically valuable. However, from a competitive standpoint, the project currently lacks a moat. With 0 stars and 4 forks, it is purely a reference implementation for the paper's findings. Frontier labs like Google (Gemini) and Cohere (Command R) are aggressively optimizing multilingual retrieval and would likely absorb these debiasing techniques into their base models or system-level RAG pipelines if the performance gains are validated. The displacement horizon is short (6 months) because query fusion techniques are easily integrated into standard RAG frameworks like LangChain or LlamaIndex. The 'defensibility' is low because it is an algorithmic tweak rather than a platform or a proprietary dataset.