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HinglishEval

Artifact for HinglishEval: Evaluating the Effectiveness of Code-generation Models on Hinglish Prompts.

Background

Hindi is one of the most widely spoken languages in the world, and the most widely spoen in India. A majority of the population in India does not speak English as their first language, and therefore language models that can understand prompts in native languages are important for wider accessibility. Hinglish is a blend of Hindi and English, with frequent usage of English words in sentences with standard Hindi grammar. This is not representative of everyday spoken Hindi for most people, but is rather common in coversations involving technical language, especially in the context of programming.

Therefore it is most natural for Hindi speaking users to prompt LLMs in Hinglish when they want to generate code, or ask for help with programming in general (like explanations or debugging). We therefore present HinglishEval, a benchmark for evaluating the effectiveness of code-generation models on Hinglish prompts.

Contributions

The HinglishEval Benchmark

Our benchmark is based on OpenAI's HumanEval Benchmark. HinglishEval contains all the problems in the HumanEval with their prompts translated to Hinglish. We used GPT-4 to translate the prompts and manually verified and corrected those translations. HinglishEval is available in JSON format in this repository as HinglishEval.json.

For each prompt in HumanEval, we translated the purpose statements (supplied as docstrings in Python) to Hinglish ensuring that the translations are idiomatic and preserve the original meaning. We do not modify function signatures or any doctests present in the docstrings.

Code Samples

We have released code samples for 18 different models from prompts in both English (HumanEval) and Hinglish (HinglishEval). These include both open- and closed-source models. Further, we provide both unsanitized (raw) and sanitized (processed) code samples. The sanitization process removes erroneous or irrelevant code and text from the outputs, and retains only the required function definitions. Note that all the code samples are generated using a temperature setting of 0 (greedy decoding).

The code samples from English prompts are available in the English Data Release and the code samples from Hinglish prompts are available in the Hinglish Data Release. These code samples will allow reliable reproduction of the results presented in our paper. We understand that running LLMs can be complicated and expensive, and hope that these code samples will help motivate further research in this area.

Evaluation of Models on HinglishEval

We evaluated 18 models on the HinglishEval dataset using following two metrics.

  • Pass@1, which measures the percentage of times the model generates correct code on the first attempt. Note that the models were run with a temperature setting of 0 (greedy decoding).

  • Item Response Theory or IRT, a statistical framework for evaluating the relative capabilities of models based on the hardness of the problems presented to them. We use a 2-parameter IRT model to compute the latent abilities (competencies) of the models from the difficulty ($\beta$) and discrimination ($\alpha$) parameters of each problem.
    Difficulty roughly measures how models perform at a problem on average, while discrimination roughly measures how well a problem differentiates between high- and low-performing models.

Usage

Follow the instructions below to reproduce the results presented in the paper.

1. Cloning the Repository

git clone https://github.com/mrigankpawagi/HinglishEval.git
cd HinglishEval

2. Setting up the Environment

Copy the /example.env file to /.env and place your API keys appropriately in this .env file. We use OpenAI, DeepInfra and HuggingFace for accessing different models and you can find instructions to obtain their respective API keys at the links below.

It is recommended to use a virtual environment for installing dependencies and running the code.

python3 -m venv hinglisheval
source hinglisheval/bin/activate
pip install -r requirements.txt

If you want to run the code for generating Hinglish translations or code samples from English prompts, you will need to download the HumanEval dataset.

curl -O https://github.com/openai/human-eval/raw/refs/heads/master/data/HumanEval.jsonl.gz
gunzip HumanEval.jsonl.gz # Extract the JSONL file

3. Running the Code

  • You can run the scripts in the /samples/codemodels/ directory to generate code samples from different models.
  • To evaluate the code generation from different models with IRT, you can either generate new responses and sanitize them or directly use the santiized samples present in the releases. The scripts for the analysis are present in the /irt/ directory.

If you have any questions or need help with using this repository, please create an issue so that we can assist you.