Passage Re-Ranking via Tournament Sorting for Faster Relevance Comparison
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Solution Overview
Problem
Existing re-ranking models for document retrieval systems face inefficiencies due to high time complexity and reduced efficiency in comparing relevance, particularly in listwise re-ranking models with large parametric models and 'lost-in-the-middle' issues in cross-encoder models.
Innovation Solution
A re-ranking system utilizing tournament sorting to evaluate relevance through an encoder-decoder structure, such as FiD or T5-base architecture, where passages are divided into groups, evaluated, and sorted in multiple stages to extract top-ranked passages efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If listwise re-ranking models using LLMs are used to improve relevance comparison capability, then the capability to compare relevance is improved, but the efficiency is reduced due to large model size
Solution Approach 1:
The patent segments the re-ranking process into multiple stages: initial retrieval to get candidate passages, then iterative tournament sorting rounds where passages are divided into groups and evaluated in batches. This segmentation allows using smaller models for evaluation while maintaining overall system effectiveness, resolving the contradiction between model capability and efficiency.
Solution Approach 2:
The patent applies partial action by evaluating only a subset of passages (top candidates from initial retrieval) through the tournament sorting process rather than all passages. This selective evaluation maintains relevance comparison quality while significantly improving efficiency by avoiding unnecessary computations on low-priority passages.
2Measurement precision
If pairwise re-ranking is used to compare relevance between documents, then the relevance comparison is improved, but the time complexity increases to O(n^2)
Solution Approach 1:
The patent segments the pairwise comparisons into iterative tournament rounds where the number of comparisons decreases in each round. Instead of performing all O(n^2) comparisons simultaneously, the system performs structured rounds with elimination, reducing total computation time while maintaining comparison quality.
Solution Approach 2:
The patent performs preliminary filtering through initial retrieval to identify candidate passages before applying the tournament sorting process. This preliminary action reduces the input size for the time-consuming pairwise comparisons, effectively lowering the overall time complexity from O(n^2) to a more manageable level.
3Device complexity
If cross-encoder models are used for re-ranking, then the model structure is simple, but position bias occurs causing lost-in-the-middle problems
Solution Approach 1:
The patent segments passages into groups of specific numbers for evaluation, ensuring that no single passage is lost in the middle of a long sequence. This segmentation maintains the simplicity of cross-encoder models while improving relevance evaluation accuracy by limiting context window size and reducing position bias effects.
Solution Approach 2:
The patent implements dynamic tournament sorting where the evaluation process adapts based on previous round results. Passages are re-grouped and re-evaluated in subsequent rounds, allowing the system to dynamically adjust to position bias issues and improve overall evaluation accuracy while maintaining model simplicity.
Data Source
AI summary
A re-ranking system for extracting passages having higher relevance to a query. Re-ranking is accomplished by performing a plurality of tournament sortings, which may include arranging first passages included in the passages into units of a specific number to divide the first passages into a plurality of groups, evaluating relevance between the first passages included in a group and a query for each group, and extracting passages having higher relevance up to a predetermined rank among the first passages to output the extracted first passages as second passages, and arranging the second passages into units of a specific number to divide the second passages into a plurality of groups, evaluating relevance between the second passages included in a group and the query for each group, and extracting passages having higher relevance up to a predetermined rank among the second passages to output the extracted second passages as third passages.