Encoder Classifier Training for Pre-Generation Hallucination Prediction
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Solution Overview
Problem
Existing large language models (LLMs) suffer from hallucinations, which are false perceptions of patterns or objects, and current research primarily focuses on post-generation analysis, neglecting the prediction of query-induced hallucinations before query generation.
Innovation Solution
The development of an encoder classifier model, dubbed HalluciBot, which predicts hallucinations by perturbing queries and employing a multi-agent Monte Carlo simulation to estimate empirical probabilities, using a consensus-aware proxy reward model for query rewriting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If post-generation analysis is used to detect hallucinations, then hallucination detection can be performed after output is generated, but the ability to predict hallucinations before query generation is lost
Solution Approach 1:
The patent applies preliminary action by training the encoder classifier model to predict hallucination probability before query generation occurs. The model uses perturbed query outputs from Monte Carlo simulations to learn patterns that indicate potential hallucinations in advance, enabling proactive identification rather than reactive detection after generation completes.
2Measurement precision
If multiple independent LLMs are used to perturb queries for statistical simulation, then empirical probability estimations of hallucinations can be derived, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the hallucination detection task into multiple independent LLM agents, each responsible for generating perturbed outputs for specific queries. This modular approach allows the system to distribute computational work across multiple simplified units rather than requiring a single complex system, while still achieving precise empirical probability estimations through aggregation of results from all segments.
3Measurement precision
If query perturbation and Monte Carlo simulation are performed, then empirical probability estimations can be obtained, but computational resources and time consumption increase
Solution Approach 1:
The patent applies partial action by performing query perturbation and Monte Carlo simulation only for a subset of training queries rather than all possible queries. The system generates perturbed outputs for a manageable number of queries to derive empirical probability estimations, balancing the need for accurate measurements with computational resource constraints. This selective approach allows the encoder classifier model to be trained effectively without exhausting computational resources.
Data Source
AI summary
A method and system for training an encoder classifier model in predicting hallucination of a machine learning (ML) model before a generation of a query. The method includes implementing a plurality of independent large language models (LLMs) that each perturb a plurality of training queries for a predetermined number (n) of times, wherein the plurality of LLMs comprises n+1; utilizing the plurality of LLMs in performing functions including: generating a perturbed output for each of the plurality of training queries by perturbing the plurality of training queries; sampling an initial training query and each of the perturbed outputs; and deriving empirical probability estimations of hallucinations via a computational statistical simulation on the sampled outputs; and training the encoder classifier model based on the derived empirical probability estimations that predicts the hallucination in the ML model associated with the query before the generation of the query.