FUTURAL: A Metasearch Platform for Empowering Rural Areas with Smart Solutions
TLDR
FUTURAL introduces a Metasearch platform using LLMs to provide a natural language interface for smart solutions, empowering rural areas.
Key contributions
- Developed FUTURAL Metasearch platform MVP for smart solutions in rural areas.
- Utilizes Large Language Models (LLMs) to create a natural language user interface.
- Focuses on a single open-source data service for initial implementation.
- Details MVP design, LLM adaptation, and comprehensive evaluation techniques.
Why it matters
This paper introduces a crucial metasearch platform leveraging LLMs to make smart solutions more accessible for rural areas. Its natural language interface significantly lowers the barrier to entry for users. This groundwork is vital for expanding the platform to integrate more services and datasets, enhancing its capacity to address diverse challenges.
Original Abstract
The FUTURAL project aims to provide a comprehensive suite of digital Smart Solutions (SS) across five critical domains to address pressing social and environmental issues. Central to this initiative is a robust Metasearch platform, which will not only serve as the primary access point to FUTURAL's solutions but also facilitate the search and retrieval of SS developed by other initiatives. This paper elaborates on the MVP implementation for the MetaSearch platform. It focuses on a single, open-source data service and harnesses the generative capabilities of Large Language Models (LLMs) to create a user-friendly natural language interface. The design of the Minimum Viable Product (MVP), the tools used for adapting LLMs to our specific application, and our comprehensive set of evaluation techniques are thoroughly detailed. The results from our evaluations demonstrate that our approach is highly effective and can be efficiently implemented in future iterations of the MVP. This groundwork paves the way for extending the platform to include additional services and diverse data sets from the FUTURAL project, enhancing its capacity to address a broader array of queries and datasets.
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