The Suvach benchmark can serve as a valuable resource for driving the development of more robust and generalizable Hindi language models beyond question answering capabilities by providing a standardized evaluation framework for assessing various NLP tasks. By expanding the benchmark to include different types of tasks such as text generation, summarization, sentiment analysis, and more, researchers and developers can evaluate the performance of their models across a wide range of linguistic tasks.
Additionally, the Suvach benchmark can be used to fine-tune existing language models or train new models specifically tailored to the nuances of the Hindi language. By leveraging the high-quality dataset generated by Suvach, researchers can improve the performance of their models on Hindi-specific tasks and enhance the overall quality of NLP applications in Hindi.
Furthermore, the insights gained from working with the Suvach benchmark can inform the development of new techniques and approaches for handling low-resource languages, paving the way for advancements in Hindi NLP research and contributing to the growth of the field as a whole.