Agentic Workflow for Synthetic Data-Based LLM/LMM Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current large language models (LLMs) and large multimodal models (LMMs) lack domain-specific knowledge, leading to generation of factually incorrect, toxic, or deceiving content, and fail to adhere to specific domain principles due to missing or conflicting information in their training data, unclear understanding of domain principles, and inefficient prompt engineering.
Innovation Solution
A framework is provided to align LLMs and LMMs with domain-specific principles through pre-training and fine-tuning using domain-specific data and instructions, and post-inference alignment processes, ensuring compliance with domain-specific rules and ethics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are trained on massive general-purpose data, then they achieve broad language understanding and generation capabilities, but they lack domain-specific knowledge and fail to adhere to domain principles
Solution Approach 1:
The training process is segmented into distinct phases: pre-training on general-purpose data to establish broad language capabilities, followed by domain-specific fine-tuning to inject domain knowledge and principles. This segmentation allows the model to acquire both general versatility and domain-specific reliability without conflict
Solution Approach 2:
Domain-specific principles and knowledge are introduced through preliminary fine-tuning instructions and synthetic training data generated before deployment. This preliminary action ensures the model internalizes domain constraints upfront, preventing hallucinations and ensuring adherence to domain principles from the start
2Measurement precision
If complex prompt engineering is used to ensure domain compliance, then model output accuracy improves, but computational cost and time consumption increase significantly
Solution Approach 1:
Domain compliance rules and constraints are pre-encoded into the model's knowledge base through fine-tuning on domain-specific instructions and synthetic data. This preliminary action eliminates the need for complex runtime prompt engineering, as the model inherently understands and follows domain principles during generation
Solution Approach 2:
Instead of using complex prompt engineering to repeatedly remind the model of domain rules, the solution creates synthetic training data that copies and embeds domain principles directly into the model's training corpus. This allows the model to learn domain compliance efficiently without requiring elaborate prompts during inference
3Reliability
If reinforcement learning with human feedback (RLHF) is applied, then model alignment with human values improves, but the process is computationally expensive and does not fully ensure domain-specific compliance
Solution Approach 1:
The solution generates synthetic training data that copies domain-specific principles, guidelines, and compliance rules into structured instruction formats. This synthetic data serves as a scalable alternative to expensive human feedback, embedding domain knowledge directly into the training corpus without requiring extensive human annotation
Solution Approach 2:
The approach changes the training parameter from human feedback signals to synthetic instruction-following data. This parameter change enables domain-specific alignment through standard supervised fine-tuning rather than resource-intensive RLHF, reducing computational energy while maintaining alignment quality
4Reliability
If domain-specific fine-tuning is performed, then model adherence to domain principles improves, but training data quality and coverage remain limited without synthetic data generation
Solution Approach 1:
The system uses an LLM to generate synthetic training data that copies and adapts domain principles from source documents into numerous instruction-response pairs. This copying process creates abundant training examples from limited domain knowledge sources, ensuring comprehensive coverage of domain principles
Solution Approach 2:
Domain principles are extracted and structured in advance to guide synthetic data generation. This preliminary action ensures that the generated training data is both voluminous and high-quality, with consistent adherence to domain principles before the fine-tuning process begins
Data Source
AI summary
An agentic workflow system and method generate question and answer pairs and prompts that may be used to aligns generative artificial intelligence (a large language model (LLM) or a large multimodal model (LMM)) with the principles of a specific domain so that the generative artificial intelligence is better able to respond to a user query in the specific domain. The system and method may also generate aligning processes that may be used to post-train an already trained generative artificial intelligence system or fine tune the training of the generative artificial intelligence system to align that generative artificial intelligence system with the principles of the specific domain. The system and method may be used to align the generative artificial intelligence system to a plurality of different domains. s