paalvibe
commented
8 months ago Jeff Reiffers :guru_badge_svg: 1:29 PM Stillte GPT spørsmål om hvordan vi kan bruke LLM på data.norge.no Using a Large Language Model (LLM) like GPT-3.5 to help users navigate and find relevant information in a large dataset like data.norge.no can be a powerful application. Here are some ways you can leverage an LLM for this purpose: Conversational Search: Allow users to interact with the system using natural language queries. Users can ask questions about specific datasets, concepts, or services, and the LLM can understand and process these queries to provide relevant information. Query Expansion: Implement query expansion to help users refine their search queries. The LLM can suggest additional keywords or related terms to broaden or narrow down the search based on the user's initial input. Natural Language Understanding: Utilize the LLM's natural language understanding capabilities to extract key entities and concepts from user queries. This can help in identifying the user's intent and providing more accurate search results. Contextual Assistance: Implement contextual assistance by allowing users to ask follow-up questions or providing additional information based on the context of the conversation. The LLM can maintain a conversational context to enhance the user experience. Summarization: Use the LLM to generate summaries of detailed pages. Instead of overwhelming users with a large amount of information, provide concise summaries that highlight key points about datasets, concepts, or services. Filtering and Sorting: Allow users to filter and sort search results using natural language commands. For example, users can ask to see datasets related to a specific domain or sorted by relevance. Interactive Exploration: Create an interactive exploration experience where users can navigate through the metadata using conversational commands. The LLM can guide users through different categories, concepts, or services based on their preferences. Recommendation Engine: Implement a recommendation engine that suggests relevant datasets, concepts, or services based on the user's historical queries or preferences. The LLM can assist in understanding user preferences and making personalized recommendations. User Guidance: Provide guidance to users on how to structure their queries for better results. The LLM can assist in guiding users to ask more effective questions to retrieve the desired information. Visualization: Integrate data visualization components that work alongside the LLM. Visual representations can help users grasp complex relationships and patterns in the metadata more efficiently.
Eller er søk nok?
For chatbot så er kanskje PaLM 2 for Chat, Chat Bison eller Chat Bison 32k noe å teste. Finn eksempel kode i så fall.