To extend the Suvach benchmark to cover a broader range of question types, such as open-ended questions or multi-hop reasoning, the methodology can be adapted to incorporate more complex question structures. For open-ended questions, the prompts can be modified to encourage free-form responses rather than selecting from predefined choices. This would require the generation of prompts that allow for varied and diverse answers, challenging the models to generate responses that are not limited to a set of options.
For multi-hop reasoning, the prompts can be designed to include multiple pieces of information that require the model to connect and reason across different contexts. This would involve creating prompts that involve multiple steps or pieces of information, testing the model's ability to perform complex reasoning tasks.
By expanding the prompts and validation criteria to accommodate these different question types, the Suvach benchmark can evolve to assess a wider range of capabilities in Hindi language models beyond extractive QA.