Domain-specific question answering in low-resource languages faces two key challenges: scarcity of annotated datasets and limited domain knowledge in general-purpose language models. In this work, we present a multi-stage finetuning strategy to adapt lightweight language models to the Hindi tourism domain by leveraging both original and synthetic training data. Synthetic question-answer pairs are generated using large LLMs (LLaMA-70B, Phi-14B) and used to augment the limited original dataset. We explore several training methodologies and analyse their impact on domain generalisation. Our results demonstrate that large models can efficiently generate synthetic data, while small models can effectively adapt to it, offering a scalable pathway for low-resource, domain-specific QA.

The dictates of big data--its inner manipulations and trends--have defined the very form of the data ecosystem since the inception of these technologies nearly a decade ago. It's become entrenched in the most meaningful dimensions of data management, implicit to all but its most mundane practices, and indistinguishable from almost any type of data leveraged for competitive advantage. As such, current momentum in the big data space isn't centered on devising new expressions of its capabilities, but rather on converging them to actualize the long sought, rarely realized, time honored IT ideal of what Cambridge Semantics CTO Sean Martin termed "interoperability. And, the more the data starts to support that, the more interesting that gets, too." The grand vision of interoperability involves the capacity to readily interchange enterprise systems and resources as needed to maximize business productivity without technological restrictions.

I have a great academic knowledge and I know typing work. I can give most efficient results . I have cleared many competition exams ( National Defnce Academy (NDA), JEE Mains, UPSEE, Airforce X,Y Group exam, Navy MR AA and many others . I have a great knowledge about MS Office,typing and academic subjects like Physics, Mathematics (10 2 level), and in Basic Electrical engineering (Graduation level) .

As part of a broader push to upgrade its technology, Comerica has implemented voice artificial intelligence tools to automate much of its contact center's interactions with consumers. The regional bank has also launched voice-based authentication. Both initiatives are continually adjusted. User experiences dictate changes, sometimes as often as daily. While Comerica's overall voice efforts have already paid off in increased efficiencies, and voice authentication has been well received, the Dallas-based regional bank says it is still early days and hopes for continuing improvement in consumer service and efficiency.

As part of a broader push to upgrade its technology, Comerica has implemented voice artificial intelligence tools to automate much of its contact center's interactions with consumers. The regional bank has also launched voice-based authentication. Both initiatives are continually adjusted. User experiences dictate changes, sometimes as often as daily. While Comerica's overall voice efforts have already paid off in increased efficiencies, and voice authentication has been well received, the Dallas-based regional bank says it is still early days and hopes for continuing improvement in consumer service and efficiency.