Evaluating Multilingual Language Models: A Comprehensive Approach

Introduction

With the increasing demand for AI-driven solutions across diverse linguistic landscapes, evaluating multilingual models is crucial [1]. This blog explores a structured approach for assessing multilingual text generation models across various Indian languages. Our evaluation framework leverages Hugging Face models and standardized techniques, ensuring a robust assessment of accuracy, coherence, and language fidelity [3].

Why Evaluate Multilingual Language Models?

Multilingual models promise versatility, but their effectiveness varies across languages [2]. Evaluating these models is necessary to:

• Ensure fluency and correctness in multiple languages.
• Detect biases or inconsistencies in language generation [4].
• Measure performance across high-resource and low-resource languages.
• Validate usability in real-world applications such as chatbots, content creation, and customer support.
• Identify areas where models struggle with contextual understanding, idiomatic expressions, and code-switching.

Challenges in Multilingual Model Evaluation

While evaluating multilingual models, several challenges arise:

• Data Scarcity: Low-resource languages have fewer training datasets [5].
• Translation Errors: Direct translations may distort meaning across languages.
• Cultural Sensitivity: Ensuring that generated responses align with cultural norms.
• Computational Complexity: Evaluating across multiple languages requires extensive computational resources.

Model Selection and Setup

For our evaluation, we selected a diverse set of multilingual models, including l3cube-pune/marathi-gpt-gemma-2b, trained for Indian languages. The evaluation process involves generating responses to a diverse set of multilingual prompts in Hindi, Tamil, Bengali, Telugu, Malayalam, Marathi, and English.

Setting Up the Environment

We utilize sglang for model inference and transformers for evaluation:

from huggingface_hub import login
import sglang as sgl
import json

login("Your API Key")

gen = "l3cube-pune/marathi-gpt-gemma-2b"
gen_llm = sgl.Engine(model_path=gen)
sampling_params = {"temperature": 0.5, "top_p": 0.95}

Multilingual Prompt Set

A diverse set of 20+ multilingual prompts was designed to test general knowledge, cultural awareness, and linguistic ability [5]. These prompts include:

Response Generation and Evaluation

Using sglang

We generate responses using sglang:

responses = []
for question in questions:
    response = gen_llm.generate(question, sampling_params, stream=False)
    answer = response.get('text', '').strip()
    responses.append({"instruction": question, "answer": answer})

Alternative: Using Transformers Library

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "l3cube-pune/marathi-gpt-gemma-2b"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

responses = []
for question in questions:
    inputs = {k: v.to(device) for k, v in tokenizer(question, return_tensors="pt").items()}
    output = model.generate(**inputs, temperature=0.8, top_p=0.95, max_length=300, do_sample=True)
    response_text = tokenizer.decode(output[0], skip_special_tokens=True)
    responses.append({"instruction": question, "answer": response_text.strip()})

Evaluation Criteria

To assess the model’s multilingual performance, we focus on:

• Fluency & Grammar: Is the generated text grammatically correct?
• Relevance: Does the response match the prompt’s intent?
• Consistency: Are similar questions answered in a uniform manner across languages?
• Bias Detection: Are there any biases or incorrect stereotypes present in the responses?
• Context Awareness: Does the model adapt responses based on cultural context?

Key Findings & Insights

Conclusion:

• Expanding datasets to include more low-resource languages.
• Enhancing cross-lingual training techniques to improve context retention.
• Exploring better evaluation metrics for multilingual AI.
• Integrating human-in-the-loop assessment for improved accuracy.

References

1. Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
2. Conneau, A., & Lample, G. (2019). Cross-lingual Language Model Pretraining. arXiv preprint arXiv:1901.07291.
3. Xue, L., Constant, N., Roberts, A., et al. (2021). mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer. arXiv preprint arXiv:2010.11934.
4. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.
5. Joshi, P., Santy, S., Budhiraja, A., Bali, K., & Choudhury, M. (2020). The State and Fate of Linguistic Diversity in NLP. arXiv preprint arXiv:2004.09095.
6. Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT.

Open Source Contribution Guidelines

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch.
3. Implement changes and add relevant tests.
4. Submit a pull request.

Visit our GitHub repository for more details.

What are your thoughts on multilingual model evaluation? Share your insights and experiences in the comments!