Kppwdfgu1/ype

Yoruba Honorific Pronoun Etiquette Benchmark

Overview

This dataset is a Yoruba-language benchmark focused on honorific pronouns, politeness markers, and socially appropriate pronoun selection.

The project is motivated by the observation that politeness is culturally and socially grounded. In Yoruba, choices among second-person forms can encode age, social status, familiarity, deference, and interpersonal relationships. An LLM may produce a grammatically possible Yoruba form while still failing to preserve the culturally appropriate level of respect.

The aim of this project is therefore not only to test Yoruba language generation, but to evaluate whether a language model can maintain socially appropriate Yoruba pronoun choices when speaker-addressee relationships and discourse contexts change.

Dataset contents

The repository contains both the raw/source dataset and the AdaptLab-adapted dataset, together with benchmark and error-analysis outputs.

Main datasets

• yoruba_pronoun_etiquette.csv --- raw/source dataset.
• Yoruba_5000_AdaptLab_READY.csv --- 5,000-row AdaptLab-adapted dataset.

Benchmark outputs

• yoruba_pronoun_benchmark_clean.csv --- cleaned benchmark data.
• yoruba_pronoun_benchmark_v4.csv --- V4 benchmark results.
• yoruba_pronoun_results_v4.csv --- pronoun-level results.
• yoruba_relationship_results_v4.csv --- results grouped by social relationship.
• yoruba_pronoun_error_matrix_v4.csv --- pronoun error matrix.

Raw dataset

The raw dataset contains controlled Yoruba examples designed around pronoun etiquette and social relationships. The examples encode contextual information such as:

• speaker/addressee relationship;
• relative age or social position;
• familiarity;
• communicative situation;
• candidate pronoun forms;
• target politeness/honorific interpretation.

The raw dataset is a controlled dataset rather than a collection of naturally occurring conversations. This distinction is important when interpreting the results.

Adapted dataset

The adapted dataset was produced using AdaptLab. The adaptation process reformulated the original examples into model-oriented prompt/completion examples while retaining the central linguistic and pragmatic task.

The adapted data includes Yoruba contexts in which the model is required to select or produce an appropriate form based on the relationship between interlocutors.

Examples include contrasts involving relationships such as:

• peer → peer;
• younger → elder;
• younger → older visitor;
• younger → leader;
• student → teacher;
• student → professor;
• adult → child;
• younger → family head;
• younger → Babaláwo;
• close colleagues;
• friends;
• siblings/close family.

Annotation scheme

The benchmark treats pronoun selection as a pragmatic classification/evaluation problem rather than purely a surface-form matching task.

The relevant annotation dimensions include:

1. Social relationship --- who is speaking to whom.
2. Discourse context --- the situation in which the utterance occurs.
3. Candidate forms --- the pronoun alternatives presented to the model.
4. Gold pronoun --- the expected socially appropriate form.
5. Adapted pronoun --- the form produced/selected after adaptation.
6. Politeness/honorific function --- whether the form reflects deference, solidarity, or a non-honorific usage.

The project distinguishes forms including ìwọ, ẹ, ẹ̀yin, yín, o, ọ, and rẹ where they occur in the benchmark.

Cultural and linguistic motivation

Politeness is treated in this project as a culturally situated component of language use. Yoruba honorific usage cannot be reduced to a simple grammatical rule because social variables such as age, status, familiarity, and relationship influence appropriate forms.

The benchmark therefore deliberately places pronoun choices inside social contexts. This is intended to test whether an LLM preserves culturally meaningful distinctions rather than simply generating a locally grammatical Yoruba expression.

The project follows the linguistic literature supplied with the research, including work discussing Yoruba honorific pronouns, age, social status, familiarity, and the relationship between politeness and cultural norms.

AdaptLab and model adaptation

AdaptLab was used as the dataset adaptation tool.

The adapted dataset was subsequently used in model adaptation/fine-tuning experiments. The AutoScientist workflow was used for the training experiment.

The reported training configuration included:

• base model: meta-llama/Llama-4-Scout-17B-16E-Instruct;
• training method: supervised fine-tuning (SFT);
• parameter-efficient training: LoRA;
• LoRA rank: 64;
• LoRA alpha: 128;
• learning rate: 0.0001;
• epochs: 4;
• batch size: max;
• learning-rate scheduler: cosine;
• warmup ratio: 0.03;
• weight decay: 0.02;
• dropout: 0;
• training on inputs: false.

The trained model was named:

adaption_yoruba_pronoun_etiquette

Benchmark methodology

The final V4 benchmark independently compares the adapted model output with the expected/gold pronoun.

From the V4 benchmark run:

• total source rows inspected: 27,780;
• relationships extracted: 2,096;
• gold pronouns identified: 2,093;
• adapted pronouns identified: 2,066;
• valid benchmark cases: 2,064;
• correct predictions: 1,705;
• incorrect predictions: 359;
• exact pronoun accuracy: 82.61%.

The benchmark also reports results by social relationship and by gold pronoun, together with an error matrix.

Key benchmark findings

Overall exact pronoun accuracy was 82.61%.

Performance was strongest for several explicitly hierarchical relationships. For example:

• younger → elder: 98.94%;
• adult → child: 100%;
• younger → older visitor: 100%;
• younger → elder neighbour: 100%;
• younger → leader: 100%;
• younger → older man: 100%;
• younger → older woman: 100%;
• younger → family head: 100%;
• student → teacher: 100%;
• student → professor: 100%;
• younger → Babaláwo: 100%.

The largest relationship category was peer → peer, with 1,767 valid cases and 80.08% accuracy. This is important because peer interactions create greater ambiguity between solidarity/informal forms and honorific forms.

By gold pronoun, the V4 benchmark reported:

• ìwọ: 72.47%;
• o: 87.38%;
• ọ: 81.05%;
• ẹ̀yin: 97.73%;
• yín: 100%;
• ẹ: 97.53%;
• rẹ: 100%.

The benchmark also identified recurring confusions among candidate forms, especially in peer-to-peer contexts.

Error analysis

The principal error pattern is not simply random Yoruba generation. Many errors involve choosing a different candidate pronoun from the set supplied in the prompt.

For example, the V4 error analysis contains cases where:

• gold = ọ, adapted = ẹ;
• gold = ìwọ, adapted = yín;
• gold = o, adapted = ẹ;
• gold = ìwọ, adapted = ẹ̀yin.

The concentration of errors in peer → peer cases suggests that socially less hierarchical interactions can be more difficult to disambiguate than strongly marked age/status relationships.

This supports the central research motivation: culturally appropriate Yoruba politeness requires sensitivity to social context, not only lexical or grammatical competence.

Intended use

This dataset is intended for:

• evaluation of Yoruba-capable language models;
• research on cultural and pragmatic alignment;
• low-resource African-language NLP;
• evaluation of honorific and politeness-sensitive generation;
• research into socially grounded pronoun selection;
• benchmarking model adaptation methods.

It should not be treated as a comprehensive representation of all Yoruba speakers, dialects, communities, or politeness practices.

Limitations

1. The raw benchmark data are controlled/constructed examples rather than a corpus of naturally occurring conversations.
2. Yoruba politeness is context-sensitive, and the benchmark cannot represent every possible social interaction.
3. Some pronoun forms are multifunctional and may require discourse information beyond a short prompt.
4. Social categories such as age, status, familiarity, and relationship may overlap in real interaction.
5. Benchmark accuracy should therefore be interpreted as performance on the defined evaluation task, not as a complete measure of Yoruba language competence.
6. Cultural and pragmatic judgments may vary across speakers and communities.

Reproducibility

The repository provides the raw and adapted datasets together with benchmark outputs so that researchers can inspect the transformation and reproduce the reported evaluation.

The benchmark results were generated with the V4 evaluation pipeline and saved as CSV files in this repository.

Citation

If you use this dataset, please cite the associated competition submission/research paper:

Bamigbala Christianah Adedoyin. Yoruba Honorific Pronoun Etiquette: Culturally Grounded Pronoun Adaptation and Benchmarking for LLMs. 2026.

Acknowledgement

This project was developed as part of an African computational linguistics/LLM adaptation research activity focused on improving the representation of Yoruba pragmatic and cultural knowledge in language models.