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arxiv: 2604.18914 · v1 · submitted 2026-04-20 · 💻 cs.CL · cs.AI· cs.LG

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MORPHOGEN: A Multilingual Benchmark for Evaluating Gender-Aware Morphological Generation

Mehul Agarwal , Aditya Aggarwal , Arnav Goel , Medha Hira , Anubha Gupta
Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:01 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords gender-aware generationmorphological generationgrammatical gendermultilingual benchmarkfirst-person constructionsFrench Arabic HindiLLM evaluation
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The pith

Multilingual LLMs show significant gaps when rewriting first-person sentences to flip grammatical gender in French, Arabic, and Hindi.

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

The paper introduces a benchmark called MORPHOGEN to test how current language models handle the grammatical rules of gender in languages where it affects verbs, pronouns, and sentence structure. The central evaluation task requires a model to take a first-person sentence and rewrite it in the opposite gender while keeping the exact meaning and form intact. By creating a large synthetic dataset for three typologically different languages and running fifteen popular models on it, the work demonstrates that these models frequently produce incorrect morphological forms. A reader would care because accurate gender handling matters for any application that generates natural text in these languages, from translation tools to conversational systems.

Core claim

MORPHOGEN provides a morphologically grounded dataset spanning French, Arabic, and Hindi. Its core GENFORM task measures whether models can transform a first-person sentence into the opposite gender without altering meaning or structure. Benchmark results on fifteen models ranging from 2B to 70B parameters reveal consistent failures in producing correct verb conjugations, pronouns, and agreement patterns.

What carries the argument

The GENFORM task, which requires models to rewrite a first-person sentence in the opposite gender while preserving its meaning and structure, acts as the diagnostic test for gender-aware morphological generation.

If this is right

  • Models require targeted improvements in morphological agreement rules to succeed at gender transformations.
  • The benchmark supplies a repeatable diagnostic that future model releases can be measured against.
  • Performance differences across languages highlight which grammatical systems current training data covers least well.
  • Insights from the results can guide development of generation systems that respect explicit and implicit gender cues.

Where Pith is reading between the lines

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

  • Extending the same transformation test to additional gendered languages could reveal whether the observed gaps are universal or language-specific.
  • Integrating the GENFORM task into training loops might produce models that handle gender morphology more reliably without separate fine-tuning stages.
  • Real-world applications such as machine translation or dialogue systems could adopt similar checks to reduce gender errors before deployment.
  • The approach of testing first-person gender flips isolates a narrow but measurable aspect of inclusivity that broader benchmarks often overlook.

Load-bearing premise

The synthetic dataset accurately mirrors real grammatical gender agreement and first-person constructions in the three languages without introducing artificial patterns.

What would settle it

High accuracy across all fifteen models on the GENFORM task for French, Arabic, and Hindi would show that the claimed gaps do not exist.

Figures

Figures reproduced from arXiv: 2604.18914 by Aditya Aggarwal, Anubha Gupta, Arnav Goel, Medha Hira, Mehul Agarwal.

Figure 1
Figure 1. Figure 1: Example illustrating how gender-based morphology differs across the three languages [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Gendered Terms Distribution in MORPHOGEN 3.2 Task Formulation For the proposed GENFORM task on MORPHOGEN, we prompt a multilingual LLM with a first-person sen￾tence to rewrite the sentence in the opposite gen￾der, i.e., from masculine to feminine or vice versa, based on the original speaker’s gender. The model must correctly apply language-specific morpholog￾ical rules while preserving the sentence’s meani… view at source ↗
Figure 3
Figure 3. Figure 3: General morphological rules for grammati [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of Sentence Frequency Per Morphological Rule for Each Language [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: △SGA (Accuracy Gap) across all models and languages (French, Arabic, Hindi) in the MORPHOGEN benchmark. Positive values indicate masculine bias, while negative values indicate feminine bias. the modified sentence with no explana￾tions or extra words. If no change is required, return the sentence exactly as it is.“ The user prompt provided the transformation instruction, depending on the speaker’s gender: •… view at source ↗
Figure 6
Figure 6. Figure 6: Rule-based and model-wise IoU metrics across all three languages. [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of results of LLAMA family of models on multiple entities in French dataset. [PITH_FULL_IMAGE:figures/full_fig_p024_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Example of results of LLAMA family of models on multiple entities in Arabic dataset. [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of results of LLAMA family of models on multiple entities in Hindi dataset. [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗
read the original abstract

While multilingual large language models (LLMs) perform well on high-level tasks like translation and question answering, their ability to handle grammatical gender and morphological agreement remains underexplored. In morphologically rich languages, gender influences verb conjugation, pronouns, and even first-person constructions with explicit and implicit mentions of gender. We introduce MORPHOGEN, a morphologically grounded large-scale benchmark dataset for evaluating gender-aware generation in three typologically diverse grammatically gendered languages: French, Arabic, and Hindi. The core task, GENFORM, requires models to rewrite a first-person sentence in the opposite gender while preserving its meaning and structure. We construct a high-quality synthetic dataset spanning these three languages and benchmark 15 popular multilingual LLMs (2B-70B) on their ability to perform this transformation. Our results reveal significant gaps and interesting insights into how current models handle morphological gender. MORPHOGEN provides a focused diagnostic lens for gender-aware language modeling and lays the groundwork for future research on inclusive and morphology-sensitive NLP.

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

2 major / 2 minor

Summary. The manuscript introduces MORPHOGEN, a multilingual benchmark for gender-aware morphological generation in French, Arabic, and Hindi. The core GENFORM task requires LLMs to rewrite first-person sentences to the opposite gender while preserving meaning and structure. A high-quality synthetic dataset is constructed across the three languages, and 15 multilingual LLMs (2B–70B parameters) are evaluated, with results showing significant performance gaps and insights into current models' handling of morphological gender.

Significance. If the synthetic dataset is shown to faithfully encode real grammatical gender agreement and meaning-preserving rewrites, this benchmark would provide a focused diagnostic for an underexplored capability in multilingual LLMs. The multi-language, multi-model evaluation could guide future work on morphology-sensitive and inclusive NLP systems.

major comments (2)
  1. [§3 (Dataset Construction)] §3 (Dataset Construction): The headline claim of model-induced gaps on GENFORM requires that the synthetic dataset accurately reflects real first-person gender agreement patterns without artifacts (e.g., unnatural verb forms or inconsistent triggers). The section must detail the construction process, any LLM-assisted generation, templates used, and validation steps such as native-speaker annotation or comparison to attested corpora.
  2. [§5 (Results)] §5 (Results): The reported 'significant gaps' across the 15 LLMs are presented without statistical significance tests, confidence intervals, or error analysis broken down by language or error type. This makes it difficult to assess whether observed differences are robust or could be dataset-induced.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'interesting insights' is vague; briefly enumerating one or two key observations (e.g., language-specific failure modes) would improve informativeness.
  2. [Results tables] Table 2 or equivalent results table: Ensure consistent reporting of per-language scores and overall averages with the same number of decimal places.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper to incorporate the suggested improvements for clarity and rigor.

read point-by-point responses

  1. Referee: [§3 (Dataset Construction)] §3 (Dataset Construction): The headline claim of model-induced gaps on GENFORM requires that the synthetic dataset accurately reflects real first-person gender agreement patterns without artifacts (e.g., unnatural verb forms or inconsistent triggers). The section must detail the construction process, any LLM-assisted generation, templates used, and validation steps such as native-speaker annotation or comparison to attested corpora.

    Authors: We agree that detailed documentation of the dataset construction is critical to support the validity of the benchmark and the observed model gaps. The current §3 provides an overview of the synthetic data generation process for first-person sentences and their gender-reversed versions across French, Arabic, and Hindi. To address this, we will substantially expand the section to include: explicit language-specific templates used for sentence generation; full details on any LLM-assisted steps (including prompts and post-editing); results from native-speaker validation (we performed annotations by multiple native speakers for grammatical accuracy, naturalness, and meaning preservation, with inter-annotator agreement metrics); and comparisons to attested examples from corpora where available. These additions will confirm the absence of artifacts in gender agreement patterns. revision: yes

  2. Referee: [§5 (Results)] §5 (Results): The reported 'significant gaps' across the 15 LLMs are presented without statistical significance tests, confidence intervals, or error analysis broken down by language or error type. This makes it difficult to assess whether observed differences are robust or could be dataset-induced.

    Authors: We acknowledge that the results section would benefit from greater statistical rigor and error breakdown to strengthen claims about model performance gaps. We will revise §5 to include: statistical significance tests (such as McNemar's test for paired model comparisons) with p-values and adjustments for multiple comparisons; confidence intervals for all reported metrics; and a comprehensive error analysis disaggregated by language (French, Arabic, Hindi) and by error categories (e.g., verb morphology errors, pronoun mismatches, semantic drift). This analysis, which we have conducted internally, shows consistent patterns that support the robustness of the gaps beyond potential dataset artifacts. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark evaluation

full rationale

The paper introduces the MORPHOGEN benchmark and the GENFORM task of rewriting first-person sentences to opposite gender while preserving meaning. It describes construction of a synthetic dataset for French, Arabic, and Hindi, then reports empirical results on 15 LLMs. There are no equations, fitted parameters, first-principles derivations, or predictions that reduce to inputs by construction. No self-citations serve as load-bearing justifications for uniqueness or ansatzes. The work is self-contained as a measurement study on the provided data; dataset quality concerns affect correctness but not circularity of any derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model or derivation; the paper is an empirical benchmark study relying on standard assumptions about language morphology and LLM evaluation.

pith-pipeline@v0.9.0 · 5492 in / 961 out tokens · 30622 ms · 2026-05-10T04:01:08.686237+00:00 · methodology

discussion (0)

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Reference graph

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