This is an automated message from the <a href=\"https://huggingface.co/librarian-bots\">Librarian Bot</a>. I found the following papers similar to this paper. </p>\n<p>The following papers were recommended by the Semantic Scholar API </p>\n<ul>\n<li><a href=\"https://huggingface.co/papers/2604.20199\">All Languages Matter: Understanding and Mitigating Language Bias in Multilingual RAG</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.05821\">CLEAR: Cross-Lingual Enhancement in Alignment via Reverse-training</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.25676\">CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.17632\">Code-Switching Information Retrieval: Benchmarks, Analysis, and the Limits of Current Retrievers</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.01733\">From BM25 to Corrective RAG: Benchmarking Retrieval Strategies for Text-and-Table Documents</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.25182\">CroSearch-R1: Better Leveraging Cross-lingual Knowledge for Retrieval-Augmented Generation</a> (2026)</li>\n<li><a href=\"https://huggingface.co/papers/2604.05684\">Improving Semantic Proximity in Information Retrieval through Cross-Lingual Alignment</a> (2026)</li>\n</ul>\n<p> Please give a thumbs up to this comment if you found it helpful!</p>\n<p> If you want recommendations for any Paper on Hugging Face checkout <a href=\"https://huggingface.co/spaces/librarian-bots/recommend_similar_papers\">this</a> Space</p>\n<p> You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: <code><span class=\"SVELTE_PARTIAL_HYDRATER contents\" data-target=\"UserMention\" data-props=\"{&quot;user&quot;:&quot;librarian-bot&quot;}\"><span class=\"inline-block\"><span class=\"contents\"><a href=\"/librarian-bot\">@<span class=\"underline\">librarian-bot</span></a></span> </span></span> recommend</code></p>\n","updatedAt":"2026-05-19T01:46:26.956Z","author":{"_id":"63d3e0e8ff1384ce6c5dd17d","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg","fullname":"Librarian Bot (Bot)","name":"librarian-bot","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":357,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.7026320099830627},"editors":["librarian-bot"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/1674830754237-63d3e0e8ff1384ce6c5dd17d.jpeg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2605.07249","authors":[{"_id":"6a0b3d798ca2d0b2563801a9","name":"Youngjoon Jang","hidden":false},{"_id":"6a0b3d798ca2d0b2563801aa","name":"Seongtae Hong","hidden":false},{"_id":"6a0b3d798ca2d0b2563801ab","name":"Hyeonseok Moon","hidden":false},{"_id":"6a0b3d798ca2d0b2563801ac","name":"Heuiseok Lim","hidden":false}],"publishedAt":"2026-05-08T00:00:00.000Z","submittedOnDailyAt":"2026-05-18T00:00:00.000Z","title":"MLAIRE: Multilingual Language-Aware Information Retrieval Evaluation Protocal","submittedOnDailyBy":{"_id":"65a4c4ed2548c41ad9b1421c","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/65a4c4ed2548c41ad9b1421c/bMQbowjHKvq-bKpzalvWm.jpeg","isPro":false,"fullname":"Youngjoon Jang","user":"yjoonjang","type":"user","name":"yjoonjang"},"summary":"Multilingual Information Retrieval is increasingly important in real-world search settings, where users issue queries over mixed-language corpora. Existing evaluations mainly reward language-agnostic semantic relevance, treating relevant passages equally regardless of language. Yet retrieval utility also depends on the language of the retrieved passages: users may prefer results they can read and verify in the query language, and query--passage language mismatch can complicate downstream grounding and answer verification in Retrieval-Augmented Generation systems. To evaluate this language-aware dimension, we introduce MLAIRE, a Multilingual Language-Aware Information Retrieval Evaluation protocol that disentangles cross-lingual semantic retrieval from query-language preference. MLAIRE constructs controlled pools with parallel passages across languages, enabling measurement of semantic retrieval accuracy and query-language preference when equivalent translations are available. We propose language-aware metrics, including Language Preference Rate (LPR) and Lang-nDCG, together with a 4-way decomposition separating semantic and query-language preference failures. Evaluating 31 dense, sparse, and late-interaction retrievers, we show that standard metrics obscure distinct behaviors: semantically strong retrievers may return correct content in a non-query language, while retrievers with stronger query-language preference may retrieve less semantically relevant passages.","upvotes":0,"discussionId":"6a0b3d798ca2d0b2563801ad","ai_summary":"Multilingual information retrieval evaluation protocol MLAIRE separates semantic retrieval accuracy from query-language preference to better assess retrieval utility across mixed-language corpora.","ai_keywords":["Multilingual Information Retrieval","Retrieval-Augmented Generation","language-aware metrics","Language Preference Rate","Lang-nDCG","cross-lingual semantic retrieval","query-language preference","dense retrievers","sparse retrievers","late-interaction retrievers"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2605/2605.07249.md"}">

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