REVIEW 3 major objections 5 minor 12 references
Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs
T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read LLM factual answer quality drops with fewer speakers and less training-data representation, and the gap appears in school-level questions across 12 languages.
desk verdict A plausible direction in multilingual QA, but the key correlation table is pseudo-replicated and the central claim is not supported by the reported statistics. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The load-bearing object is a benchmark of 54 factual questions with hand-curated target words (for example, 'sunlight,' 'carbon dioxide,' and 'glucose' for 'What is photosynthesis?'), which makes factuality measurable as keyword coverage. Two evaluation tracks carry the argument: manual bilingual raters scored keyword presence, incorrectness on a 1-3 scale, and extraneous content on a 1-5 scale using machine-translated responses, and automated analysis searched for the same target words in original and translated output, counted extracted keywords, and measured repetition. The explanatory mechanism is a proxy: number of speakers stands in for how much training data a language has, and this proxy is correlated with every feature that reflects factual completeness.
What would settle it
Take a language with many speakers but very little high-quality web text, or few speakers with a large online footprint, run the same 54-question benchmark on the same models, and compare its target-word coverage with a corpus-derived token count for that language; if coverage follows speaker counts rather than actual token counts, the training-representation interpretation is wrong.
Extended reading notes
Core claim
The paper's central claim is stated in its conclusion: LLM responses are less factual in languages that have fewer speakers and are less represented in available training material. Concretely, in manual evaluation the 70B model achieved 97.48% target-keyword coverage in English but 69.25% in Hebrew and 77.25% in Hindi, with higher incorrectness and extraneous scores outside English; the 8B model showed a wider gap, with Hebrew at 27.28% versus English at 94.44%. Automated target-word detection over all 12 languages produced Spearman correlations with speaker count that are positive and statistically significant for both model sizes, and the larger model showed additional correlations with response length and keyword repetition. The paper treats these results as confirmation that lower-speaker and lower-resource languages receive less complete and less accurate factual answers.
Load-bearing premise
The paper treats the number of speakers of a language as a stand-in for how much training data the model saw in that language, without actually measuring training data, and several speaker counts are disputed; if that proxy fails, the result only links factuality to speaker counts, not to representation.
Editorial extensions
If this is right
- English remains the best-served language on factual school-level questions, so educational tools relying on Llama 3.1-style models will supply the most complete answers to English-speaking students.
- In lower-speaker languages such as Hebrew and Hindi, students can receive answers with missing key facts, extra irrelevant text, and a higher rate of incorrect content.
- Scaling from the 8B model to the 70B model improves absolute coverage in every language but does not remove the gap, so larger models alone will not make multilingual factuality equitable.
- Factuality benchmarks for multilingual models should include keyword-level correctness and extraneous-content checks rather than measuring only fluency or translation quality.
Reading between the lines
- If the speaker-count proxy is really capturing training-data representation, then a language's factual question-answering score could serve as a cheap public probe of how well a model was trained on that language, without needing access to the training corpus.
- A direct testable extension would be to add high-quality educational text in one low-speaker language to a model's training data and check whether its 54-question coverage moves toward English levels; the paper's claim predicts it should.
- The manual evaluation covers only four languages, so the automated correlations across all 12 languages should be rechecked with human ratings on more languages to see whether the manual pattern generalizes.
- The causal reading that training-data representation drives the gap is not proven by correlation; if actual corpus token counts diverge from speaker counts, the result would reduce to a statement about speaker counts rather than representation.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper evaluates the factuality of Llama 3.1 8B and 70B responses to 54 middle/high school factual questions in 12 languages. The authors measure keyword coverage and response properties, and report significant Spearman correlations between speaker counts and these features, concluding that LLMs are less factual in less-spoken languages and that this exacerbates existing biases. The paper also includes a small manual evaluation on a subset of languages.
Significance. If the central finding were statistically valid, it would be a useful contribution to multilingual LLM evaluation and to the discussion of equity in AI-assisted education. The benchmark of 54 curriculum-based questions with hand-curated target keywords is a reasonable starting point, as is the attempt to combine automatic and manual evaluation. However, the main statistical argument rests on correlations with a language-level variable that are tested over response-level observations, which invalidates the reported p-values. The manuscript also overinterprets speaker counts as training-data representation and makes a causal 'exacerbation' claim that the data cannot support. These issues must be resolved before the headline conclusion can be accepted.
major comments (3)
- [Results, Table 2] The p-values in Table 2 are computed over all 54×12 responses per model, but the independent variable (speaker count) takes only 12 distinct values, one per language. This pseudo-replication inflates the effective sample size from 12 to 648, producing p-values such as 1.5e-27 that cannot be achieved with an honest n=12. The correlation analysis should be performed on language-level aggregates (e.g., per-language mean response features) with n=12, or with a mixed-effects model that treats language as a random effect. Until then, the claim that the correlations are statistically significant is not supported.
- [Conclusion; Appendix] The conclusion asserts that the results confirm that LLMs are less factual in languages 'less-represented in available training material,' but the paper never measures training data. The proxy used is speaker count, which the Appendix acknowledges is disputed and averaged across sources. For languages such as Tulu, Māori, and Haitian Creole, speaker counts and digital corpus sizes are known to diverge; absent any direct measurement of training data, the results can only be interpreted as correlations with speaker counts, not with training-data representation.
- [Abstract; Conclusion] The claim that LLMs 'exacerbate existing biases against rare languages' is not supported by the experimental design. The study provides a cross-sectional correlation between speaker counts and answer quality; it does not compare model outputs to any baseline bias or measure amplification, so 'exacerbate' is an unsupported causal conclusion.
minor comments (5)
- [Results, Table 1] The text states that the manual evaluation was conducted on English, Hindi, and Hebrew, but Table 1 also shows a Farsi row; please clarify which languages were manually evaluated and why only LLaMA 70B is reported for Farsi.
- [Methodology] The benchmark questions were presented in 12 languages, but the paper does not describe how the English questions were translated into the other 11 languages; this is a potentially important methodological detail and should be reported.
- [Results, Table 2] Table 2 does not state the sample size used for each correlation; please add the number of observations.
- [Results, Table 2] The feature names 'Original Target Words Count' and 'Translated Target Words Count' are ambiguous; please clarify whether these are binary indicators of presence or actual counts of matched keywords.
- [Abstract; References] The abstract contains a typo ('continues of grow'), and the link to the benchmark and code appears only as 'here' without an actual URL or reference.
Circularity Check
No circularity: the study is an empirical measurement against external covariates, with no fitted parameter renamed as a prediction and no load-bearing self-citation chain.
full rationale
The paper's derivation chain is empirical rather than definitional. The authors hand-curate 54 factual questions and target keywords from a school curriculum, present the questions to Llama 3.1 models in 12 languages, translate responses with Google Translate, detect target keywords and KeyBERT terms, and correlate the resulting response features with an external variable: the number of speakers of each language. No parameter is fitted to a subset of the data and then relabeled as a prediction; the correlations are measured against an independent covariate (speaker counts) whose values are not derived from model outputs. There is no self-citation that carries a load-bearing argument; the cited Related Work is contextual and not used to establish the paper's measured correlations. The claim that fewer speakers proxy for less training-data representation is an assumption or interpretation, not a circular reduction, because the target-word coverage metric is not defined in terms of speaker counts and speaker counts are not derived from the evaluation data. The appendix's admission that speaker figures are disputed and averaged is a data-quality caveat, and the skeptic's pseudo-replication concern is a statistical validity issue about independence of observations, not an instance of a conclusion being equivalent to its inputs by construction. Thus no circular step can be exhibited, and the appropriate score is 0.
Assumptions & free parameters
assumptions (3)
- domain assumption Google Translate preserves target factual content adequately across all 12 languages for keyword detection.
- domain assumption Number of speakers of a language is a valid proxy for its representation in Llama training data.
- domain assumption Hand-picked target words are necessary and sufficient for a correct answer.
Cite this review
Pith. "Pith review of Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs." pith.science (2026). https://pith.science/paper/2SOEOOK5
@misc{pith2026250603051,
author = {Pith},
title = {Pith review of: Facts Do Care About Your Language: Assessing Answer Quality of Multilingual LLMs},
year = {2026},
howpublished = {\url{https://pith.science/paper/2SOEOOK5}},
note = {Machine review of arXiv:2506.03051}
}
read the original abstract
Factuality is a necessary precursor to useful educational tools. As adoption of Large Language Models (LLMs) in education continues of grow, ensuring correctness in all settings is paramount. Despite their strong English capabilities, LLM performance in other languages is largely untested. In this work, we evaluate the correctness of the Llama3.1 family of models in answering factual questions appropriate for middle and high school students. We demonstrate that LLMs not only provide extraneous and less truthful information, but also exacerbate existing biases against rare languages.
Figures
Figures from the paper (1 more)
Reference graph
Works this paper leans on
-
[1]
On the diversity of synthetic data and its impact on training large language models
Hao Chen, Abdul Waheed, and Others. On the diversity of synthetic data and its impact on training large language models. arXiv preprint arXiv:2410.15226, 2024
arXiv 2024
-
[2]
Llm-powered data augmentation for enhanced crosslingual performance
Alham Fikri Aji Chenxi Whitehouse, Monojit Choudhury. Llm-powered data augmentation for enhanced crosslingual performance. EMNLP Proceedings, 2023
work page 2023
-
[3]
Evaluating and Mitigating Linguistic Discrimination in Large Language Models , May 2024
Guoliang Dong, Haoyu Wang, Jun Sun, and Xinyu Wang. Evaluating and Mitigating Linguistic Discrimination in Large Language Models , May 2024. URL http://arxiv.org/abs/2404.18534. arXiv:2404.18534 [cs]
arXiv 2024
-
[4]
Task Dynamics of Prior Training Influence Visual Force Estimation Ability During Teleoperation
Maarten Grootendorst. Keybert: Minimal keyword extraction with bert. arXiv preprint arXiv:2004.13226, 2020
work page Pith review arXiv 2004
-
[5]
Yiqiao Jin, Mohit Chandra, Gaurav Verma, Yibo Hu, Munmun De Choudhury, and Srijan Kumar. Better to Ask in English : Cross - Lingual Evaluation of Large Language Models for Healthcare Queries , October 2023. URL http://arxiv.org/abs/2310.13132. arXiv:2310.13132 [cs]
arXiv 2023
-
[7]
Edoardo Ponti et al. Modeling language variation and universals: A survey on morphosyntactic information in language models. Computational Linguistics, 2019
work page 2019
-
[8]
Breena R. Taira, Lisa C. Diamond, Vanessa Kreger, and Aristides Orue. A pragmatic assessment of google translate for emergency department instructions. Olive View-UCLA Medical Center; Memorial Sloan Kettering Cancer Center, 2021
work page 2021
-
[9]
Document- Level Machine Translation with Large Language Models , October 2023
Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang, Dian Yu, Shuming Shi, and Zhaopeng Tu. Document- Level Machine Translation with Large Language Models , October 2023. URL http://arxiv.org/abs/2304.02210. arXiv:2304.02210 [cs]
arXiv 2023
Show all 12 references
-
[10]
Factcheck- Bench : Fine - Grained Evaluation Benchmark for Automatic Fact -checkers, April 2024
Yuxia Wang, Revanth Gangi Reddy, Zain Muhammad Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, and Preslav Nakov. Factcheck- Bench : Fine - Grained Evaluation ...
2024 arXiv
-
[11]
Languages used on the Internet --- W ikipedia , the free encyclopedia
Wikipedia. Languages used on the Internet --- W ikipedia , the free encyclopedia. http://en.wikipedia.org/w/index.php?title=Languages\ [Online; accessed 09-October-2024]
2024
-
[12]
Quantifying multilingual performance of large language models across languages
Wei Xie, Mengnan Du, et al. Quantifying multilingual performance of large language models across languages. arXiv preprint arXiv:2404.11553, 2024
2024 arXiv
-
[13]
Extrapolating Large Language Models to Non - English by Aligning Languages , October 2023
Wenhao Zhu, Yunzhe Lv, Qingxiu Dong, Fei Yuan, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, and Lei Li. Extrapolating Large Language Models to Non - English by Aligning Languages , October 2023. URL http://arxiv.org/abs/2308.04948. arXiv:2308.04948 [cs]
2023 arXiv
Reviewed August 7, 2026 · model on record in the stance chip above.
Discussion (0). Sign in to comment.