Context Reduction for Domain-Specific LLM Question Answering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large language models (LLMs) struggle to provide accurate responses to domain-specific questions due to limited training data and the inefficiency of handling longer prompts, leading to inaccurate responses and increased processing costs.
Innovation Solution
Implement context reduction techniques to identify the most relevant sentences from a context document using reinforcement learning, reducing the prompt size to fit within the LLM's token limit while maintaining accuracy, by ranking sentences based on similarity to the query and dynamically selecting the optimal number of sentences through Q-learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire context document is provided to the language model, then the model has access to complete information, but the processing cost increases and the prompt may exceed token limits
Solution Approach 1:
The patent segments the context document into individual sentences and selectively includes only the most relevant sentences in the prompt, rather than providing the entire document. This segmentation allows the system to maintain response accuracy by including necessary information while reducing processing costs by excluding redundant content.
Solution Approach 2:
The patent extracts and includes only the most relevant sentences from the context document based on their similarity to the query. By taking out only the essential information needed to answer the query, the system reduces prompt size and processing cost while maintaining the reliability needed for accurate responses.
2Reliability
If more sentences from the context document are included in the prompt, then the response accuracy improves, but the prompt size increases beyond token limits
Solution Approach 1:
The patent divides the context document into discrete sentence units and selectively includes only the top-k most relevant sentences in the prompt. This segmentation enables the system to control prompt size while maintaining accuracy by including only the necessary number of sentences that fit within token limits.
Solution Approach 2:
The patent dynamically adjusts the number of sentences included in the prompt based on query complexity and token limits. By changing the parameter of sentence count adaptively, the system optimizes the balance between prompt size and response accuracy, ensuring the prompt remains within acceptable length constraints.
3Reliability
If the entire context document is processed, then all information is available for answering, but the time to generate a response increases
Solution Approach 1:
The patent performs preliminary ranking of sentences by relevance to the query before generating the response. By pre-ordering sentences based on their similarity to the query and selecting only the top-k relevant ones, the system reduces the amount of information the language model needs to process, thereby reducing response generation time while maintaining information completeness for accurate answers.
4Reliability
If domain-specific context is provided to improve accuracy, then the response quality improves, but the complexity of the system increases
Solution Approach 1:
The patent introduces an intermediary sentence ranking mechanism that bridges the query and the language model. This intermediary component ranks and selects relevant sentences from the context document based on query similarity, providing domain-specific context without requiring complex system architecture. The intermediary simplifies the overall system by using a straightforward relevance-based filtering approach.
Data Source
AI summary
Methods and systems for context reduction include identifying a context document relating to a query. A number of sentences of the context document to preserve is determined. The sentences of the context document are ranked according to respective similarities between the sentences and the query. A reduced context is generated that preserves the determined number of highest ranked sentences of the context document and eliminates other sentences from the context document. The query is executed with a language model, including the reduced context in a prompt, to generate a response.