README.md

---
language:
- yo
license: other
pretty_name: Yoruba Honorific Pronoun Etiquette Benchmark
size_categories:
- 1K\<n\<10K
tags:
- yoruba
- linguistics
- pragmatics
- politeness
- honorifics
- pronouns
- low-resource-language
- african-languages
- llm-evaluation
- cultural-alignment
---

# 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.