LLM Search Integration
The Posters.science platform integrates Large Language Models (LLMs) to provide advanced AI-powered discovery capabilities, enabling users to find relevant scientific posters through natural language queries and intelligent content analysis.
Smart Search Feature
Upon clicking on the "Smart Search" button, users will be taken to the Smart Search feature's page. On this page, they will be able to ask specific questions, e.g., "What new discoveries were presented at ARVO 2025 regarding the connection between the eye and Alzheimer's disease?". The Smart Search feature runs a query through the Posters.science database to generate an answer and provide a list of the top 5 most relevant posters.
Example Queries
- "What discoveries related to Alzheimer's were presented at ARVO 2025?"
- "Show me recent research on machine learning in medical imaging"
- "Find posters about climate change from European conferences"
- "What are the latest findings in cancer immunotherapy?"
Technical Implementation
Infrastructure
- Local GPU Compute: On-premises LLM processing
- Retrieval-Augmented Generation (RAG): Context-aware responses
- Vector Database: Semantic similarity search
- Knowledge Base: Domain-specific information
LLM Processing Pipeline
- Query Analysis: Intent and entity extraction
- Context Retrieval: Relevant poster content gathering
- Prompt Construction: Context-aware prompt generation
- LLM Inference: Model processing and response generation
- Response Validation: Quality and accuracy checking
- Result Formatting: User-friendly response presentation
AI Capabilities
Content Understanding
- Semantic Analysis: Deep content comprehension
- Topic Classification: Automatic subject categorization
Query Processing
- Intent Classification: User goal identification
- Entity Recognition: Key concept extraction
- Query Expansion: Related term inclusion
- Contextual Understanding: Conversation flow maintenance
Response Generation
- Summarization: Concise content summaries
- Explanation: Research context and significance
- Recommendations: Related poster suggestions
- Citation: Proper source attribution