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README.md

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+---
+language:
+- fr
+- fon
+pretty_name: French-Fon Code-Switching Evaluation Benchmark
+task_categories:
+- question-answering
+tags:
+- code-switching
+- fon
+- french
+- multilingual
+- nlp
+- low-ressource-language
+- llm-evaluation
+size_categories:
+- n<1K
+license: mit
+---
+
+# French-Fon Code-Switching Evaluation Benchmark
+
+## Overview
+
+This dataset is a benchmark designed to evaluate the contextual understanding of small language models in French-Fon code-switching scenarios.
+
+The benchmark focuses on situations in which French and Fon (Fongbé) are used within the same interaction, with particular attention to cases where critical information required to answer a question is provided in Fon.
+
+The benchmark was developed as part of an academic research project on the evaluation of language models in multilingual and low-resource language contexts.
+
+## Research Question
+
+The main research question is:
+
+> How well can a small open-weight language model maintain contextual understanding when users switch between French and Fon within the same interaction?
+
+## Model Evaluated
+
+The benchmark was evaluated using:
+
+**Qwen/Qwen3-4B-Instruct-2507**
+
+The model contains approximately 4 billion parameters and was selected because it is an open-weight language model within the required range for this study.
+
+## Benchmark Design
+
+The benchmark contains:
+
+- 40 base scenarios
+- 4 experimental conditions
+- 160 total evaluation instances
+
+The 40 scenarios are distributed across four categories:
+
+- Daily-life situations
+- Reasoning
+- Context tracking
+- Local and cultural context
+
+Each category contains 10 base scenarios.
+
+## Experimental Conditions
+
+Each scenario was evaluated under four conditions.
+
+### 1. French baseline (`fr`)
+
+The complete task is presented in French.
+
+This condition provides a baseline for measuring the model's performance in French.
+
+### 2. Fon-only (`fon`)
+
+The relevant information and task are presented in Fon.
+
+This condition evaluates the model's ability to understand and use information expressed in Fon.
+
+### 3. French-Fon code-switching (`fr_fon`)
+
+French and Fon are combined within the same interaction.
+
+This condition evaluates the model's ability to maintain contextual understanding when switching between the two languages.
+
+### 4. Critical information in Fon (`critical_fon`)
+
+The final question is presented in French, while the information necessary to answer the question is provided only in Fon.
+
+This condition specifically tests whether the model can identify, understand and reason over critical information expressed in Fon.
+
+## Evaluation
+
+Each model response was manually evaluated using a binary correctness criterion:
+
+- `1` = correct
+- `0` = incorrect
+
+The evaluation primarily considered whether the model produced the expected semantic answer.
+
+A qualitative error taxonomy was also used to analyze incorrect responses.
+
+## Error Taxonomy
+
+The following error categories were defined:
+
+- **E1 — Wrong Fon understanding:** incorrect interpretation of information expressed in Fon.
+- **E2 — Information loss:** relevant contextual information is lost or modified.
+- **E3 — Code-switching interpretation error:** incorrect interpretation of the interaction when French and Fon are combined.
+- **E4 — Reasoning error:** the relevant information is available but the model fails to perform the required reasoning.
+- **E5 — Hallucination:** the model introduces unsupported information.
+- **E6 — Partially correct:** the response contains part of the expected information but is incomplete.
+- **E7 — Refusal or incomprehension:** the model states that it cannot understand or answer the task.
+- **E8 — Other:** errors that do not fit the previous categories.
+
+## Translation Verification
+
+The 40 Fon translations used in the benchmark were manually verified before the final evaluation.
+
+The translations were considered valid and no correction was required.
+
+Automatic translation was therefore not treated as the gold standard for evaluation.
+
+## Results
+
+The benchmark produced the following overall accuracy:
+
+**31.9%**
+
+Performance by experimental condition:
+
+| Condition | Accuracy |
+|---|---:|
+| French (`fr`) | 97.5% |
+| Fon (`fon`) | 7.5% |
+| French-Fon (`fr_fon`) | 5.0% |
+| Critical information in Fon (`critical_fon`) | 17.5% |
+
+Performance by scenario category:
+
+| Category | Accuracy |
+|---|---:|
+| Context | 37.5% |
+| Reasoning | 37.5% |
+| Culture | 27.5% |
+| Daily life | 25.0% |
+
+## Error Analysis
+
+Among the 109 incorrect responses, the main observed error types were:
+
+| Error type | Count | Percentage |
+|---|---:|---:|
+| E1 | 43 | 39.45% |
+| E3 | 34 | 31.19% |
+| E7 | 20 | 18.35% |
+| E4 | 11 | 10.09% |
+| E2 | 1 | 0.92% |
+
+The two dominant error types were E1 and E3, which together represented approximately 70.6% of all observed errors.
+
+The results show a substantial degradation when Fon is involved in the task compared with the French baseline.
+
+The French baseline reached 97.5% accuracy, whereas the Fon-only and French-Fon conditions reached 7.5% and 5.0%, respectively.
+
+The critical-information-in-Fon condition reached 17.5%.
+
+These results support the hypothesis that the model experiences substantial difficulty maintaining contextual understanding when information is expressed in Fon.
+
+However, the hypothesis that the critical-information-in-Fon condition would necessarily produce the lowest performance was not confirmed, since the French-Fon condition obtained the lowest accuracy.
+
+## Limitations
+
+This benchmark has several limitations.
+
+First, it contains 160 evaluation instances and therefore represents a relatively small evaluation set.
+
+Second, the benchmark focuses on French and Fon and should not be generalized automatically to all African languages or multilingual settings.
+
+Third, the evaluation uses manually constructed scenarios and therefore reflects the design choices of the researchers.
+
+Finally, the results concern the evaluated model and benchmark conditions and should not be interpreted as a general statement about all language models or about the overall ability of AI systems to understand Fon.
+
+## Intended Use
+
+This dataset can be used for:
+
+- Evaluating multilingual language models.
+- Studying code-switching between French and Fon.
+- Studying low-resource language understanding.
+- Analyzing contextual understanding in multilingual interactions.
+- Reproducing the experiments described in the associated research project.
+
+## Dataset Structure
+
+The repository contains evaluation scenarios, model responses, annotations, result tables, visualizations and experimental notebooks.
+
+## Citation
+
+If you use this benchmark in academic work, please cite the associated research project.
+
+## License
+
+This dataset is released under the MIT License.