README.md

metadata

configs:
  - config_name: gsm8k_araeng
    data_files:
      - split: test
        path:
          - gsm8k/gsm8k_araeng.csv
  - config_name: gsm8k_chieng
    data_files:
      - split: test
        path:
          - gsm8k/gsm8k_chieng.csv
  - config_name: gsm8k_hineng
    data_files:
      - split: test
        path:
          - gsm8k/gsm8k_hineng.csv
  - config_name: gsm8k_spaeng
    data_files:
      - split: test
        path:
          - gsm8k/gsm8k_spaeng.csv
  - config_name: lid_chieng
    data_files:
      - split: test
        path:
          - lid/lid_chieng.csv
  - config_name: lid_fridut
    data_files:
      - split: test
        path:
          - lid/lid_fridut.csv
  - config_name: lid_gereng
    data_files:
      - split: test
        path:
          - lid/lid_gereng.csv
  - config_name: lid_guaspa
    data_files:
      - split: test
        path:
          - lid/lid_guaspa.csv
  - config_name: lid_hineng
    data_files:
      - split: test
        path:
          - lid/lid_hineng.csv
  - config_name: lid_hokman
    data_files:
      - split: test
        path:
          - lid/lid_hokman.csv
  - config_name: lid_mareng
    data_files:
      - split: test
        path:
          - lid/lid_mareng.csv
  - config_name: lid_msaea
    data_files:
      - split: test
        path:
          - lid/lid_msaea.csv
  - config_name: lid_nepeng
    data_files:
      - split: test
        path:
          - lid/lid_nepeng.csv
  - config_name: mmlu_araeng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_araeng.csv
  - config_name: mmlu_beneng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_beneng.csv
  - config_name: mmlu_chieng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_chieng.csv
  - config_name: mmlu_duteng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_duteng.csv
  - config_name: mmlu_freeng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_freeng.csv
  - config_name: mmlu_gereng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_gereng.csv
  - config_name: mmlu_hineng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_hineng.csv
  - config_name: mmlu_mareng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_mareng.csv
  - config_name: mmlu_nepeng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_nepeng.csv
  - config_name: mmlu_spaeng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_spaeng.csv
  - config_name: mmlu_tameng
    data_files:
      - split: test
        path:
          - mmlu/mmlu_tameng.csv
  - config_name: mt_araeng_eng
    data_files:
      - split: test
        path:
          - mt/mt_araeng_eng.csv
  - config_name: mt_beneng_eng
    data_files:
      - split: test
        path:
          - mt/mt_beneng_eng.csv
  - config_name: mt_chieng_chi
    data_files:
      - split: test
        path:
          - mt/mt_chieng_chi.csv
  - config_name: mt_chieng_eng
    data_files:
      - split: test
        path:
          - mt/mt_chieng_eng.csv
  - config_name: mt_hineng_eng
    data_files:
      - split: test
        path:
          - mt/mt_hineng_eng.csv
  - config_name: mt_hokman_man
    data_files:
      - split: test
        path:
          - mt/mt_hokman_man.csv
  - config_name: mt_mareng_eng
    data_files:
      - split: test
        path:
          - mt/mt_mareng_eng.csv
  - config_name: mt_spaeng_eng
    data_files:
      - split: test
        path:
          - mt/mt_spaeng_eng.csv
  - config_name: ner_guaspa
    data_files:
      - split: test
        path:
          - ner/ner_guaspa.csv
  - config_name: ner_hineng
    data_files:
      - split: test
        path:
          - ner/ner_hineng.csv
  - config_name: ner_msaea
    data_files:
      - split: test
        path:
          - ner/ner_msaea.csv
  - config_name: ner_spaeng
    data_files:
      - split: test
        path:
          - ner/ner_spaeng.csv
  - config_name: pos_chieng
    data_files:
      - split: test
        path:
          - pos/pos_chieng.csv
  - config_name: pos_fridut
    data_files:
      - split: test
        path:
          - pos/pos_fridut.csv
  - config_name: pos_hineng
    data_files:
      - split: test
        path:
          - pos/pos_hineng.csv
  - config_name: pos_spaeng
    data_files:
      - split: test
        path:
          - pos/pos_spaeng.csv
  - config_name: sa_beneng
    data_files:
      - split: test
        path:
          - sa/sa_beneng.csv
  - config_name: sa_hineng
    data_files:
      - split: test
        path:
          - sa/sa_hineng.csv
  - config_name: sa_maleng
    data_files:
      - split: test
        path:
          - sa/sa_maleng.csv
  - config_name: sa_mareng
    data_files:
      - split: test
        path:
          - sa/sa_mareng.csv
  - config_name: sa_nepeng
    data_files:
      - split: test
        path:
          - sa/sa_nepeng.csv
  - config_name: sa_spaeng
    data_files:
      - split: test
        path:
          - sa/sa_spaeng.csv
  - config_name: sa_tameng
    data_files:
      - split: test
        path:
          - sa/sa_tameng.csv
  - config_name: truthfulqa_araeng
    data_files:
      - split: test
        path:
          - truthfulqa/truthfulqa_araeng.csv
  - config_name: truthfulqa_chieng
    data_files:
      - split: test
        path:
          - truthfulqa/truthfulqa_chieng.csv
  - config_name: truthfulqa_hineng
    data_files:
      - split: test
        path:
          - truthfulqa/truthfulqa_hineng.csv
  - config_name: truthfulqa_spaeng
    data_files:
      - split: test
        path:
          - truthfulqa/truthfulqa_spaeng.csv
license: apache-2.0
language:
  - zh
  - en
  - es
  - hi
  - de
  - nl
  - fy
  - fr
  - ar
  - bn
  - mr
  - ne
  - ta
  - ml
  - gn
  - ne
size_categories:
  - 10K<n<100K
task_categories:
  - text-generation
  - question-answering
  - translation
  - text-classification
tags:
  - code-mixing
  - multilingual
  - llm-evaluation
  - benchmark

ℹ️Dataset Card for CodeMixBench

[EMNLP'25] CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages

Code-mixing is a linguistic phenomenon where multilingual speakers switch or mix two or more languages within a single utterance or conversation. To evaluate LLMs’ comprehension of multilingual code-mixed texts, we introduce CodeMixBench, a benchmark comprising eight tasks across 18 languages.

🔎Dataset Details

Our benchmark comprises synthesized datasets targeting knowledge reasoning, mathematical reasoning, and truthfulness tasks, along with LID, POS, NER, SA, and MT tasks, which have been adapted from open-source studies.

CodeMixBench vs. Others

Previous benchmarks, such as GLUECoS and LinCE, primarily focus on traditional NLP tasks and are limited to a small number of languages. LinCE includes four language pairs and five NLP tasks: Language Identification(LID), Part of Speech (POS), Named Entity Recognition (NER), Sentiment Analysis (SA), and Machine Translation (MT). In contrast, GLUECoS covers two language pairs, lacks the MT task, but adds Question Answering (QA) and Natural Language Inference (NLI). Our review of recent codemixing studies indicates that research extends beyond the language pairs used in LinCE and GLUECoS. Therefore, we expanded to 16 language pairs and introduced tasks better suited for evaluating LLMs, such as Multi-Choice, Math, and Truthfulness, resulting in a total of eight tasks.

Statistics of Synthetic Datasets

For knowledge reasoning, we developed the code-mixed MMLU (CM-MMLU) based on the MMLU test set, featuring multiple-choice questions from 57 subjects to assess the model's comprehensive knowledge reasoning abilities. For mathematical reasoning, we created the code-mixed GSM8K (CM-GSM8K), derived from the GSM8K test set, which evaluates mathematical reasoning capabilities with each question including step-by-step solutions. For truthfulness assessment, we constructed the code-mixed TruthfulQA (CM-TruthfulQA) using 817 multiple-choice questions from the TruthfulQA test set.

Statistics of Collected Datasets

We selected and reconstructed 30 datasets from existing open-source projects. To comprehensively evaluate the performance of large models on code-mixing, we aimed to encompass a diverse range of language families and tasks, prioritizing manually annotated datasets. Ultimately, we cover traditional NLP tasks such as Language Identification (LID), Named Entity Recognition (NER), Part-of-Speech tagging (POS), Sentiment Analysis(SA), and Machine Translation (MT), and cover 16 languages from seven language families: Germanic(en, de, nl, fy), Sino-Tibetan (zh, hok), Romance(es), Afro-Asiatic (msa, ea), Indo-Aryan (hi, bn, ne,mr), Dravidian (ta, ml), and Tupian (gn).

Experience Results

We evaluate three families of LLMs on CodeMixBench, revealing consistent underperformance across all models on code-mixing datasets involving language pairs from different language families. However, enhancements in training data size, model scale, post-training, and few-shot learning can improve LLM performance on code-mixing datasets.

🚀Load CodeMixBench

Taking the GSM8K task with mixed Chinese and English, gsm8k_chieng, as an example.

from datasets import load_dataset

dataset_dict = load_dataset('CodeMixBench/CodeMixBench', data_files={'test': './gsm8k/gsm8k_chieng.csv'})

📍Dataset Sources

• Repository: https://github.com/Jeromeyluck/CodeMixBench/
• Paper: CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages

Setup

1. Follow these steps to set up your development environment:

   git clone [email protected]:Jeromeyluck/CodeMixBench.git
   cd CodeMixBench
   
   conda create -n CodeMixBench python=3.9
   conda activate CodeMixBench
   pip install -r requirements.txt
   

2. To launch an llm for testing:

   python ./test_model.py \
     --dataset lid_guaspa \
     --expid lid_guaspa_all_0shot \
     --model gpt-3.5-turbo \
     --shot 5 \
     --api sk-********************* \
     --url https://****************
   

   • dataset: select the dataset (e.g., lid_gereng, lid_spaeng, ner_hineng).
   • expid: define the ID of the test, the results file will be named after this ID.
   • model: the model you test. The default model is gpt-3.5-turbo.
   • shot: use for few-shot test (by default it will be 1).
   • api: API Key (default key will be OPENAI_API_KEY defined in system path).
   • url: API function provider's URL.

🔗Citation

BibTeX:

 @misc{yang2025codemixbenchevaluatingcodemixingcapabilities,
    title={CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages}, 
    author={Yilun Yang and Yekun Chai},
    year={2025},
    eprint={2507.18791},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2507.18791}, 
}