Indi-RomCoM: Code-Mixed Benchmark for Evaluating LLMs on Romanized Indic-English Instructions

1. 1Build a dataset of Romanized Indic-English code-mixed language instructions
2. 2Run experiments to evaluate LLMs on the Indi-RomCoM benchmark
3. 3Configure LLMs to handle code-mixed language inputs
4. 4Test the performance of LLMs on the benchmark
5. 5Apply fine-tuning techniques to improve LLM performance on code-mixed language tasks

NLP engineers and researchers on a team can benefit from this benchmark to evaluate and fine-tune their LLMs for better performance on multilingual tasks, especially in Romanized Indic-English code-mixed language scenarios

💡 Evaluating LLMs on code-mixed language benchmarks like Indi-RomCoM can significantly improve their performance on multilingual tasks

📚 Evaluate LLMs on Romanized Indic-English instructions with Indi-RomCoM benchmark! 🚀