Language Model Reasoning Plans for Parallel Knowledge Extraction
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Solution Overview
Problem
Existing language model-guided processes for large-scale reasoning face challenges in computational efficiency, latency, and memory usage due to the autoregressive nature of neural networks, making parallelization difficult and impractical for large-scale knowledge bases.
Innovation Solution
A single language model neural network is used both as a planner and executor to generate and execute a reasoning process, including filtering, extraction, grouping, and summarization operations, leveraging parallel computing units to reduce latency and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a language model neural network is used for large-scale reasoning over knowledge bases, then reasoning capability and response quality are improved, but computational efficiency and latency deteriorate due to the autoregressive nature of the model
Solution Approach 1:
The patent segments the reasoning process into distinct operational phases: planning (generating the reasoning plan) and execution (carrying out the plan). This segmentation allows the language model to generate a comprehensive reasoning plan once, then enables multiple computing units to execute different parts of the plan in parallel, thereby maintaining high reasoning capability while improving computational efficiency and reducing latency for large-scale knowledge base queries.
2Measurement precision
If the language model neural network processes large-scale knowledge bases, then data extraction quality and precision are improved, but memory usage and resource consumption increase
Solution Approach 1:
The patent extracts and separates the planning function from the execution function. The language model neural network is used only for generating the reasoning plan (taking out the planning task), while the actual execution is performed by multiple computing units processing the knowledge base in parallel. This extraction allows high-quality data extraction to be maintained through careful plan generation while reducing memory usage during execution by distributing the workload across multiple units rather than loading everything into a single model's memory.
3Speed
If parallelization is attempted with traditional autoregressive neural networks, then computational speed may be improved, but implementation complexity and practical feasibility worsen
Solution Approach 1:
The patent applies preliminary action by having the language model generate a complete reasoning plan before execution begins. This plan explicitly outlines the steps, operations, and data flow needed to answer the query. Because the plan is prepared in advance, multiple computing units can then execute different parts of the plan in parallel without requiring complex coordination or communication during execution, greatly simplifying the parallelization implementation while maintaining high computational speed.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a response to a query. One of the methods includes receiving query data defining a query from a user; processing a network input that comprises the query data, using a language model neural network, to generate a set of planning parameters of plan to be executed by the language model neural network to generate the response to the query from the user; executing the plan defined by the set of planning parameters using the language model neural network; and generating, using the language model neural network, and based on the data extracted from the documents classified as being relevant to the query, a response to the query.