Fortune Analytics Language Model for Domain-Specific Q&A Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
General-purpose large language models struggle to maintain high accuracy and contextual fidelity in domain-specific business-related contexts, lacking robustness for precise and reliable responses to queries requiring financial literacy, market awareness, and organizational decision-making processes.
Innovation Solution
A Fortune Analytics Language Model (FALM) is developed, leveraging a curated knowledge base from professional journalism to provide domain-specific question and answering capabilities, incorporating fine-tuning engines for article-based, topic-based, metric-based, and persona-based question-answering, with guardrails for ethical and accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If general-purpose large language models are used, then broad language understanding capability is achieved, but accuracy and contextual fidelity in domain-specific business contexts deteriorates
Solution Approach 1:
The patent segments the language model into two distinct components: a general-purpose LLM for broad language understanding and a domain-specific fine-tuned model for business analytics. This segmentation allows each component to excel at its specialized function, resolving the contradiction between general adaptability and domain-specific precision.
Solution Approach 2:
The patent applies local quality by enhancing specific parts of the system with domain-specific knowledge. The fine-tuned model incorporates business analytics expertise, financial literacy, and market awareness in specific domains, while the general LLM maintains broad language capabilities. This localized enhancement resolves the accuracy issue in domain contexts without sacrificing general versatility.
2Adaptability or versatility
If general-purpose large language models are used, then broad language processing capability is achieved, but reliability for financial and market analysis deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the model on general language data and then fine-tuning it with domain-specific business analytics data before deployment. This preliminary preparation ensures the model has both broad language processing capability and reliable domain-specific knowledge, eliminating the need for runtime adaptation and ensuring consistent reliability.
Solution Approach 2:
The patent creates a composite model structure combining general-purpose language processing capabilities with domain-specific business analytics expertise. This composite approach integrates the strengths of general LLMs with specialized knowledge bases, achieving both versatility and reliability in financial and market analysis contexts.
3Measurement precision
If domain-specific fine-tuning is performed, then accuracy in specific domains is improved, but model complexity and training requirements increase
Solution Approach 1:
The patent segments the training process into distinct phases: initial pre-training on general language data, followed by domain-specific fine-tuning on business analytics data. This segmentation manages training complexity by breaking it into manageable stages, each with specific objectives and data requirements, while achieving high accuracy in domain-specific queries.
Solution Approach 2:
The patent performs preliminary pre-training on general language data before domain-specific fine-tuning. This preliminary action establishes a strong foundational language understanding that simplifies subsequent domain-specific training, reducing the overall complexity and computational requirements compared to training from scratch.
4Reliability
If multiple fine-tuning aspects are implemented, then domain-specific performance is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal fine-tuning framework that handles multiple domain-specific aspects (article-based QA, topic-based QA, metric-based QA, persona-based QA) through a single multi-functional system. This universal approach improves domain-specific performance across various business analytics tasks while managing system complexity through unified architecture and standardized processing pipelines.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Method, application server, and non-transitory computer-readable medium for question- and-answer generation using a fortune analytics language model (FALM) are disclosed. In an aspect, a pre-trained large language model (LLM) is generated using information associated with a particular practice area. Further, fine tuning of the pre-trained LLM is performed for a plurality of different aspects to generate the FALM. A user query is then received from a client device. A plurality of new queries are then regenerated based upon the user query. Furthermore, the new queries are executed using the FALM to receive a plurality of answers, each answer of the plurality of answers corresponds with a new query of the new queries. Each answer is then ranked. Also, one or more answers are presented on a display of the client device, the one or more answers are displayed according to a predefined criterion and based upon the ranking.