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Prasenjit Mitra

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Book Open access Jul 2026

The Alignment Gap: A Benchmark Demonstrating the Lack of Cross-Lingual Mapping in Dialect-Specialized Language Models - The Case of Ehugbo

Cross-lingual information retrieval (CLIR) for low-resource dialects remains underexplored, despite millions of speakers worldwide. This work addresses a critical gap by introducing the first information retrieval (IR) benchmark resource for Ehugbo (the Afikpo dialect of Igbo with \textasciitilde 150,000 speakers in Nigeria), constructed from a high-quality parallel multimodal corpus: 1 hour of transcribed Ehugbo Bible audio aligned with standard English translations. This parallel design enables rigorous evaluation of retrieval models across language pairs. Our benchmark reveals a surprising and counterintuitive finding: the ''Alignment Gap'', where African-centric foundation models (Serengeti, Afro-XLMR, AfriBERTA) that excel at linguistic familiarity with Igbo achieve <5% retrieval accuracy, while global models like LaBSE achieve 85% despite less exposure to the language family. Through diagnostic analysis (t-SNE visualizations, tokenization studies, error patterns), we show that regional models lack cross-lingual alignment bridges despite deep language understanding, while global models achieve language invariance through explicit translation supervision. This finding has immediate implications for the design of multilingual systems: pre-training diversity alone is insufficient for dialectal IR. We release our Ehugbo corpus, results, and evaluation splits on GitHub to enable future work on dialect-specific fine-tuning and alignment strategies for African languages.

Ukachi Agnes Eze-Mbey, V. Olufemi, A. Bahizire et al. · 0 citations
Book Open access Jul 2026

From Translation to Retrieval: Evaluating LLM-Based Information Retrieval for Hausa and Fongbe

LLM-based reranking has been evaluated for some African languages, but whether LLM-based query expansion helps or hurts retrieval for low-resource African languages remains an open question. Adeyemi et al. evaluated cross-lingual LLM reranking for Hausa with English queries, yet to our knowledge no published work has evaluated LLM-based query expansion for Hausa or Fongbe specifically, and no IR evaluation resources were found for Fongbe. This study builds upon our prior work on LLM translation quality evaluation and data augmentation for corpus expansion in Hausa and Fongbe. We propose experiments that compare LLM reranking and query expansion against BM25 and multilingual dense retrieval baselines (mDPR, mContriever) for Hausa and Fongbe using three commercial LLMs. Our completed translation quality assessment confirms a large LLM-capability gap between the two languages (best BLEU: Hausa 15.75 vs. Fongbe 7.18; human scores 4.5/5 vs. 2.2/5), and our data augmentation experiments across three encoder models show that LLM-generated text consistently hurts downstream NER tasks while producing mixed effects on POS tagging, motivating careful language-specific IR evaluation. We plan to use the CIRAL test collection for Hausa and to construct a new cross-lingual test set derived from Fongbe Wikipedia data following the AfriCLIRMatrix methodology.

Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit Mitra · 0 citations