Intent-Based Document Ranking for Interpretable LLM Responses
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional information retrieval methods struggle to accurately rank documents based on user intent due to reliance on linear ranking and keyword matching, leading to irrelevant results and overwhelming amounts of data, especially in large language models (LLMs) where responses are difficult to interpret and validate.
Innovation Solution
An intent-based data generation platform that employs a multi-stage process combining semantic similarity ranking and intent-based re-ranking to prioritize documents aligned with user intent, using vector representations and categorical labels to refine document rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear ranking and keyword matching methods are used for information retrieval, then the system is simple to implement, but the relevance and accuracy of retrieved documents to user intent deteriorates
Solution Approach 1:
The ranking process is divided into multiple stages: initial retrieval stage, semantic similarity ranking stage, and intent-based re-ranking stage. Each stage handles specific aspects of the ranking problem, allowing the system to achieve high accuracy without overwhelming complexity in a single monolithic component.
Solution Approach 2:
The patent introduces semantic similarity as an additional dimension beyond traditional keyword matching. By computing vector representations and measuring semantic distances, the system adds a new dimension to the ranking space that captures meaning rather than just keyword overlap, improving accuracy without sacrificing the original keyword-based approach.
2Adaptability or versatility
If extensive training models with multiplicity of inputs are used in LLMs, then the model's general-purpose language generation capability is improved, but the interpretability and validity of responses deteriorates
Solution Approach 1:
The patent introduces an information retrieval intermediary layer between user queries and the LLM. This intermediary retrieves and ranks relevant documents based on semantic similarity and intent, providing interpretable evidence that mediates between the complex LLM processing and the user's need for understandable, valid responses.
Solution Approach 2:
The system implements feedback loops where retrieved documents and their rankings are used to guide and validate LLM responses. The intent classification and semantic similarity metrics provide feedback mechanisms that make the LLM's generation process more transparent and verifiable against ground truth documents.
3Quantity of substance
If conventional information retrieval returns all matching documents, then completeness of information is improved, but the overwhelming amount of data and irrelevance to user intent worsens
Solution Approach 1:
The patent performs preliminary semantic similarity ranking and intent-based re-ranking before presenting documents to the user. This preliminary filtering action identifies and prioritizes the most relevant documents based on their semantic relationship to the query and their alignment with detected user intent, reducing the need for users to sift through large volumes of less relevant information.
Data Source
AI summary
The systems and methods disclosed herein generates responses generated by artificial intelligence (AI) models such as large language models (LLM) using intent-based rankings of retrieved information. The systems and methods disclosed herein receives an output generation request for the generation of an output using a set of AI models. Using a first AI model, a set of documents are retrieved using the received output generation request. The set of documents are partitioned into chunks. The chunks are ranked using a distance between the vector representation of the received output generation request and the vector representation of each chunk. A second AI model classifies the output generation request and chunks using an intent of the respective output generation request or chunk, and generates a second set of rankings using the intents. The set of AI models generate a response using the second set of rankings.