This note explains SENATOR, a framework that lets a language model find and repair its own knowledge gaps. It first states the problem, where models answer poorly in specialized domains because they miss complex domain relationships, which then shows up as hallucination. The architecture is broken down into its parts, a knowledge graph, Monte Carlo Tree Search, structural entropy, synthetic data, and fine-tuning. The process runs in two steps. First it finds gaps by measuring uncertainty on cloze questions and exploring suspect regions of the graph. Then it patches them with targeted synthetic question and answer pairs followed by fine-tuning. A closing list of benefits sums up why the approach is more efficient than retraining a whole model.
Problem
- LLMs answer inaccurately or incorrectly in specialized domains because they haven't mastered complex domain-specific relationships
- This leads to "hallucination", where the model produces inaccurate information
The SENATOR Framework
SENATOR (Structural Entropy-guided Knowledge Navigator) is a framework that lets an LLM check and patch its own knowledge gaps systematically.
Architecture
- Knowledge Graph: Represents the relationships between pieces of knowledge
- Monte Carlo Tree Search (MCTS): A search algorithm for exploring regions of knowledge
- Structural Entropy (SE): Measures how well the model understands chains of linked knowledge
- Synthetic Data: Artificially generated training data
- Fine-Tuning: Tuning the model on the new data
Process
Step 1: Find knowledge gaps
- Measure uncertainty: Measure how confident the model is on cloze (fill-in-the-blank) questions
- Use Structural Entropy (SE): Assess how well the model understands the chains of linked knowledge in the knowledge graph
- Apply Monte Carlo Tree Search (MCTS): Efficiently explore suspect regions of knowledge, focusing on areas where the model's uncertainty is high
Step 2: Patch the gaps
- Generate synthetic training data (Synthetic QA): Create questions and answers aimed precisely at the knowledge regions the model hasn't mastered
- Targeted fine-tuning: Retrain the model on the synthetic data so it relearns exactly the content it needs, without noise from irrelevant information
Benefits
- Detects and fixes knowledge gaps automatically and systematically
- Focuses on the model's weak regions instead of retraining the whole model
- Improves accuracy and reduces hallucination in specialized domains
- Makes fine-tuning more efficient by concentrating only on the needed knowledge
See also: Fine Tuning Techniques · LLM Overview