LLM Agent Orchestration for Regulatory Filing Information Extraction
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Solution Overview
Problem
Existing systems struggle to efficiently extract and aggregate specific types of information from multiple regulatory filings to generate structured responses to user queries, particularly due to the unstructured nature of the data and varying means of structuring insights across different filings.
Innovation Solution
Utilizing Large Language Models (LLMs) as agents to transform queries into action items, generate plans, select optimal execution paths, and orchestrate expert sub-agents to perform tasks, aggregating outputs into structured responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple LLM agents are deployed to extract and aggregate information from regulatory filings, then information extraction accuracy and response quality improve, but system complexity and computational resources increase
Solution Approach 1:
The system divides the information extraction task into multiple specialized LLM agents, each responsible for specific aspects of regulatory filing analysis. The query replication agent splits user queries into action items, expert LLM sub-agents handle specific extraction tasks, and the plan orchestrator coordinates their work, allowing complex extraction to be broken into manageable segments that improve accuracy without overwhelming a single system component
Solution Approach 2:
The plan orchestrator LLM agent serves as an intermediary that coordinates between user queries and multiple expert LLM sub-agents. It manages the workflow by receiving queries, determining which experts to invoke, aggregating their outputs, and presenting unified responses, thereby simplifying the interaction complexity while enabling sophisticated multi-agent collaboration
2Loss of time
If LLM agents process multiple regulatory filings in parallel, then response time to user queries decreases, but computational energy consumption increases
Solution Approach 1:
The query replication LLM agent performs preliminary actions by transforming user queries into structured action items before the main processing begins. This pre-processing organizes the task requirements and enables subsequent parallel execution to be more efficient, reducing both time and energy by avoiding redundant processing steps
Solution Approach 2:
The system employs partial action by activating only the necessary expert LLM sub-agents based on the specific query and filing type, rather than processing all filings through all experts. The plan orchestrator selectively engages experts based on query requirements, reducing unnecessary computational energy consumption while maintaining fast response times through targeted parallel processing
3Loss of information
If regulatory filings are processed through multiple reasoning stages, then insight quality improves, but processing complexity increases
Solution Approach 1:
The processing pipeline is segmented into distinct reasoning stages: query replication transforms queries into action items, plan generation creates execution strategies, plan selection chooses optimal approaches, and plan orchestration coordinates expert execution. Each stage handles a specific aspect of information extraction, improving insight quality through systematic multi-stage reasoning while managing complexity through clear stage boundaries and specialized functions
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for using large language models (LLMs) to extract information from regulatory filings and to aggregate the extracted information in order to generate structured responses to user queries are disclosed. The method includes: receiving a query from a user; using a first LLM to transform the query into a set of action items; using a second LLM to generate plans for responding to each action item; using a third LLM to select an optimum plan for executing the set of action items; using a fourth LLM to assemble a set of fifth LLMs to act as expert LLM sub-agents to perform respective tasks based on the optimum plan; obtaining, from each expert LLM sub-agent, a respective output that results from a performance of the corresponding task; and aggregating the outputs into a single response to the query.