Contextualized Prompting With Entity Linking for Personalized LLMs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generative language models are large and require significant computational resources and training data, making it difficult to tune them effectively for specific users, and existing approaches to adapt them often exceed the contextual memory limit.
Innovation Solution
Process a user's search log to tag strings with linked entity resource identifiers, populate a linked entity database, and generate contextualized prompt data structures to condition the generative language model for personalized responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative language models are trained for new tasks, then task performance is improved, but computational resources and training data requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and processing user search logs before actual query processing. Entity linkers pre-process search logs to extract and store entity-resource identifier mappings in databases, creating reusable knowledge structures that eliminate the need for repeated training when adapting to new user-specific tasks
Solution Approach 2:
Instead of training the entire generative language model for each task, the system creates simplified copies or representations of task-specific knowledge through entity linkages and contextualized prompts. These lightweight representations capture essential task information without requiring full model retraining, significantly reducing computational overhead
2Adaptability or versatility
If user-specific context information is added to personalize responses, then response relevance is improved, but contextual memory limit is exceeded
Solution Approach 1:
The system extracts only the most relevant entity identifiers and contextual information from user search logs, separating essential personalization data from redundant information. By selecting and storing only key entity-resource mappings that are most useful for query personalization, the system maintains response relevance while staying within contextual memory limits
Solution Approach 2:
The system transforms extensive user search log data into a compressed dimensional representation using entity-resource identifier mappings. Instead of storing raw search query text and full contextual information, the system maps entities to unique identifiers and stores structured relationships in databases, dramatically reducing the dimensional space required to represent user-specific context
Data Source
AI summary
The disclosed concepts relate to contextualization of generative language models. In some implementations, a linked entity database is populated with entity resource identifiers of entities extracted from a search log by an entity linker. A contextualized prompt data structure is generated based on the linked entity database, e.g., by including linked entity context information in the contextualized prompt data structure. A response to the contextualized prompt data structure is received, where the response is conditioned on the linked entity context information.