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arxiv: 2604.12477 · v1 · submitted 2026-04-14 · 💻 cs.CL · cs.AI

Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe

Mahounan Pericles Adjovi , Roald Eiselen , Prasenjit Mitra This is my paper

Pith reviewed 2026-05-10 15:01 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords low-resource languagesdata elicitationprompt strategiesHausaFongbelarge language modelsWest African languages
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The pith

Strategic prompting extracts 6-41 times more usable Hausa and Fongbe text from GPT-4o Mini than from Gemini.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether different prompting strategies can draw usable text in Hausa and Fongbe out of large language models trained mostly on high-resource data. It runs six elicitation tasks on two commercial models and counts how many target-language words each produces per API call. Results show one model is far more productive overall, yet the best task type shifts with the language. Readers care because low-resource languages lack the digital text needed to train practical NLP tools, and this route could lower the cost of building such datasets. The authors release the generated text and code for others to use or improve.

Core claim

By comparing six elicitation task types on GPT-4o Mini and Gemini 2.5 Flash, the authors establish that GPT-4o Mini extracts 6-41 times more usable target-language words per API call. Optimal strategies differ by language: Hausa benefits from functional text and dialogue prompts, while Fongbe requires constrained generation prompts.

What carries the argument

Systematic comparison of six elicitation task types across two commercial LLMs, scored by usable target-language words generated per API call.

If this is right

  • Hausa data extraction works best with prompts that involve functional text and dialogue.
  • Fongbe data extraction improves when prompts tightly constrain what the model generates.
  • Commercial LLM APIs can supply scalable volumes of low-resource language text when the right model and task are chosen.
  • Released corpora and code allow direct reuse or further refinement by other groups.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the generated text performs well in real applications, the same prompting patterns could be tried on dozens of other low-resource languages.
  • Model providers may face new questions about whether and how their training data can be repurposed for language documentation.
  • Combining outputs from several models or adding light human filtering could raise quality without losing the efficiency gain.

Load-bearing premise

That text produced by the models counts as usable for downstream NLP tasks even without detailed checks against native-speaker data or error analysis.

What would settle it

Train a concrete NLP system such as a machine translation model or part-of-speech tagger on the extracted corpora and compare its accuracy to the same system trained on human-collected Hausa or Fongbe text.

Figures

Figures reproduced from arXiv: 2604.12477 by Mahounan Pericles Adjovi, Prasenjit Mitra, Roald Eiselen.

Figure 1
Figure 1. Figure 1: Extraction efficiency: usable target￾language words per API call, by model, language, and task type. GPT-4o Mini dominates across all conditions; the gap is most extreme for Fongbe [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Document-level target language detection [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Fongbe diacritic analysis by task type. Left: diacritic-to-alphabetic ratio; Right: proportion of outputs containing any diacritics. Constrained generation reliably elicits diacritics from both mod￾els [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Character trigram cosine similarity be￾tween generated Hausa text and MasakhaNER 2.0 training text, used as a proxy for potential memo￾rization. All values are well below 0.15 (dashed line), suggesting outputs represent novel genera￾tion rather than training data reproduction [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sentence-level code-switching rates by model, language, and task type. Constrained generation consistently achieves the lowest code￾switching. Fongbe shows much higher rates than Hausa across all tasks. Appendix C. Full Evaluation Summary [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Large language models (LLMs) are trained on data contributed by low-resource language communities, yet the linguistic knowledge encoded in these models remains accessible only through commercial APIs. This paper investigates whether strategic prompting can extract usable text data from LLMs for two West African languages: Hausa (Afroasiatic, approximately 80 million speakers) and Fongbe (Niger-Congo, approximately 2 million speakers). We systematically compare six elicitation task types across two commercial LLMs (GPT-4o Mini and Gemini 2.5 Flash). GPT-4o Mini extracts 6-41 times more usable target-language words per API call than Gemini. Optimal strategies differ by language: Hausa benefits from functional text and dialogue, while Fongbe requires constrained generation prompts. We release all generated corpora and code.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper investigates strategic prompting to extract usable text data from commercial LLMs (GPT-4o Mini and Gemini 2.5 Flash) for Hausa and Fongbe. It compares six elicitation task types and reports that GPT-4o Mini yields 6-41 times more usable target-language words per API call than Gemini, with language-specific optimal strategies (functional text and dialogue for Hausa; constrained generation for Fongbe). All generated corpora and code are released.

Significance. If the usability of extracted words is rigorously validated, the work offers a practical, scalable approach to addressing data scarcity for low-resource languages by mining LLMs, with potential impact on NLP for West African languages. The empirical comparison across models and strategies, combined with the open release of data and code, supports reproducibility and further investigation.

major comments (3)
  1. [Abstract and Results] Abstract and Results: The central quantitative claim (GPT-4o Mini extracts 6-41 times more usable words per API call) depends entirely on the authors' internal judgment of 'usable' target-language words, yet no section describes the criteria, human validation by native speakers, inter-annotator agreement, error typology, or comparison against gold native-speaker text.
  2. [Methods] Methods: The six elicitation task types are introduced but lack sufficient detail on exact prompt templates, how 'constrained generation' or 'functional text' were operationalized differently per language, total API calls made, or any post-generation filtering steps, preventing independent assessment of the reported multipliers and strategy rankings.
  3. [Results] Results: No quantitative quality metrics (e.g., grammaticality scores, perplexity on held-out data, or downstream task performance lift) are reported for the extracted words; the released corpora are offered for inspection, but the paper itself supplies none, leaving the language-specific optimal-strategy claims unsupported by evidence beyond the authors' unvalidated counts.
minor comments (2)
  1. [Abstract] Abstract: Speaker population figures ('approximately 80 million' for Hausa, '2 million' for Fongbe) are stated without citation; adding references would improve context.
  2. The manuscript would benefit from an explicit Limitations section discussing risks of LLM hallucinations, code-switching, or dialectal bias in the generated text for these languages.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which highlight important areas for improving the clarity and rigor of our work. We address each major comment below and specify the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract and Results] The central quantitative claim (GPT-4o Mini extracts 6-41 times more usable words per API call) depends entirely on the authors' internal judgment of 'usable' target-language words, yet no section describes the criteria, human validation by native speakers, inter-annotator agreement, error typology, or comparison against gold native-speaker text.

    Authors: We agree that the definition and assessment of 'usable' words requires explicit documentation. In the revised manuscript, we will add a dedicated subsection in Methods that defines the usability criteria (predominantly target-language content with coherent structure and minimal code-switching) and provides concrete examples of usable versus non-usable outputs for both languages. We did not perform formal native-speaker validation, inter-annotator agreement, or error typology analysis, as the study prioritized comparative elicitation efficiency over corpus validation. We will add this as an explicit limitation and note that the released corpora enable independent validation by others. No direct comparison to gold native text was conducted. revision: partial

  2. Referee: [Methods] The six elicitation task types are introduced but lack sufficient detail on exact prompt templates, how 'constrained generation' or 'functional text' were operationalized differently per language, total API calls made, or any post-generation filtering steps, preventing independent assessment of the reported multipliers and strategy rankings.

    Authors: We acknowledge the need for greater methodological transparency. The revised manuscript will include all six exact prompt templates in a new appendix. We will also expand the Methods section to describe the language-specific operationalization of each strategy (including differences in constraints for Hausa versus Fongbe), report the precise number of API calls per model-language-strategy combination, and detail all post-generation steps such as duplicate removal and basic language filtering. revision: yes

  3. Referee: [Results] No quantitative quality metrics (e.g., grammaticality scores, perplexity on held-out data, or downstream task performance lift) are reported for the extracted words; the released corpora are offered for inspection, but the paper itself supplies none, leaving the language-specific optimal-strategy claims unsupported by evidence beyond the authors' unvalidated counts.

    Authors: We recognize that additional metrics would provide stronger support. In the revision, we will report basic quantitative indicators in Results, such as the percentage of target-language tokens (via automated detection) and average text length per strategy. We did not compute grammaticality scores, perplexity, or downstream task performance, as suitable benchmarks and held-out data for Hausa and Fongbe are limited and would have required resources outside the scope of this elicitation-focused study. We will discuss this limitation and emphasize that the open corpora facilitate such analyses. The strategy rankings remain grounded in the yield of usable words, which will be better supported by the expanded Methods details. revision: partial

Circularity Check

0 steps flagged

No circularity: purely empirical counts from API experiments

full rationale

The paper conducts a direct empirical comparison of six prompting strategies across two LLMs, measuring usable target-language words per API call via explicit filtering criteria applied to generated outputs. No equations, derivations, fitted parameters, or self-citations are invoked as load-bearing premises. The central results (multipliers of 6-41x and language-specific strategy rankings) are computed from observed token counts rather than any self-referential definition or renamed input. The work is self-contained against external benchmarks: replication requires only repeating the documented prompts against the same commercial APIs. No step reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical study with no mathematical derivations, free parameters, axioms, or invented entities; relies on standard assumptions that LLMs encode linguistic knowledge and that API outputs can be filtered for usability.

pith-pipeline@v0.9.0 · 5449 in / 1121 out tokens · 44294 ms · 2026-05-10T15:01:37.102916+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

19 extracted references · 19 canonical work pages · 1 internal anchor

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    Mining Large Language Models for Low-Resource Language Data: Comparing Elicitation Strategies for Hausa and Fongbe

    Introduction Natural Language Processing (NLP) technologies remain largely inaccessible to speakers of most African languages due to severe data scarcity (Joshi et al., 2020). Languages such as Fongbe, a national language of Benin, and Hausa, widely spoken across West Africa, suffer from limited digital text resources despite having millions of speakers. ...

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    How does the linguistic quality of LLM- generated text vary across elicitation task types for Hausa and Fongbe?

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    Which elicitation strategies produce the great- est lexical diversity and domain coverage per API call?

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    Do optimal elicitation strategies differ between languages with different levels of LLM sup- port? 1.2. Summary of Contributions •A systematic taxonomy of LLM elicitation strategies for low-resource language data ex- traction,evaluatedacrosssixtasktypesfortwo typologically distinct West African languages (Section 3). •An empirical comparison of two commer...

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    African Language NLP Resources Research on African language technology has ac- celerated significantly since 2019

    Related Work 2.1. African Language NLP Resources Research on African language technology has ac- celerated significantly since 2019. The Masakhane project established a participatory approach to machine translation across more than 30 African languages (Nekoto et al., 2020). Subse- quent efforts produced standardized benchmarks: MasakhaNER 2.0 for NER acr...

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    Conti- nentalsurveysconfirmthatmostAfricanlanguages lacksufficientcorpora(Hedderichetal.,2021)

    given its international media presence, while Fongbe falls closer to the lowest tiers (0–1). Conti- nentalsurveysconfirmthatmostAfricanlanguages lacksufficientcorpora(Hedderichetal.,2021). Our workproposesLLM-baseddataextractionasascal- able complement to manual corpus construction. 2.2. LLMs for Low-Resource Languages Robinson et al. (2023) showed that C...

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    how well do LLMs translate into lan- guage X?

    outperform multilingual baselines but focus on comprehension rather than generation. A critical gap persists: none of these studies investigate whichtypesof prompts maximize ex- tractable language data. Our work reframes the question from “how well do LLMs translate into lan- guage X?” to “which prompting strategies extract the most usable data from LLMs ...

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    Methodology We design a controlled experiment comparing six elicitation task types across two LLMs and two lan- guages. All prompts, scripts, and evaluation code arereleasedpublicly(see AppendixA).Fullprompt structures and examples are provided in Appendix D. 3.1. Elicitation Task Taxonomy Table 1 summarizes six task types, each probing a different dimens...

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    Results We report results from 600 API calls (150 prompts ×2 models×2 languages). 4.1. Output Validity Table 2 reports the percentage of outputs exceed- ing the 20-token minimum and the average word count per condition. Gemini GPT-4o Mini Task Fon Hau Fon Hau Creative 28/18 76/27 100/90 100/104 Functional 36/17 68/20 100/153 100/205 Structured 40/18 56/20...

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    Optimal Elicitation Strategies by Language Our results confirm that optimal strategies differ substantially between languages (RQ3)

    Discussion 5.1. Optimal Elicitation Strategies by Language Our results confirm that optimal strategies differ substantially between languages (RQ3). ForHausa, functional text and dialogue yield the most usable words (190–205 per call with GPT-4o Mini), while constrained generation and topicswitchingachievethehighestlanguagefidelity (100% for both models)....

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    First, we eval- uate only two commercial LLMs; the performance gapweobservemaynotgeneralizetoopen-source or African-language-focused models

    Limitations This study has several limitations. First, we eval- uate only two commercial LLMs; the performance gapweobservemaynotgeneralizetoopen-source or African-language-focused models. Second, our evaluation relies entirely on automatic metrics; hu- man evaluation by native speakers is essential, particularly for Fongbe where GlotLID misidenti- fies 4...

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    Ourworkaimstoredirectencoded knowledge back to these communities

    Ethics Statement Data provenance and community benefit.We acknowledge that LLMs were trained on data con- tributed by language communities, often without explicitconsent. Ourworkaimstoredirectencoded knowledge back to these communities. All gener- ated data will be released under CC-BY-4.0. Quality and potential harms.LLM-generated text may contain errors...

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    The evaluation pipeline, including the GlotLID-based language fidelity assessment, is provided for full reproducibility

    Data and Code Availability All generated corpora, prompts, generation scripts, and evaluation code will be made publicly available upon acceptance under a CC-BY-4.0 license. The evaluation pipeline, including the GlotLID-based language fidelity assessment, is provided for full reproducibility

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    We acknowledge the Masakhane community for their foundational contributions to African NLP resources, particularly the MasakhaNER 2.0 and MasakhaPOS datasets used in our evaluation

    Acknowledgements The authors thank the reviewers for their construc- tive feedback. We acknowledge the Masakhane community for their foundational contributions to African NLP resources, particularly the MasakhaNER 2.0 and MasakhaPOS datasets used in our evaluation. This publication was developed as part of the Center for Inclusive Digital Transformation o...

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