LLM Hallucination Probability Prediction Before Generation
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Solution Overview
Problem
Current methods fail to predict the probability of hallucination in Large Language Models (LLMs) before generation, leading to computational waste and lack of user accountability for hallucinatory queries.
Innovation Solution
Implement a platform, language, and cloud agnostic probability of hallucination predicting module using a generative model trained with a simulation algorithm to estimate the likelihood of hallucination before query generation, employing Multi-Agent Monte Carlo Simulation to derive an empirical estimate of hallucination rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If LLMs generate outputs without pre-generation hallucination prediction, then computational resources are consumed for every query, but hallucination probability cannot be assessed beforehand leading to computational waste
Solution Approach 1:
The system performs preliminary action by training a generative model using simulation algorithms before actual query processing. The model pre-learns hallucination patterns and probabilities from simulated data, enabling it to predict hallucination likelihood for new queries without requiring full generation and post-processing verification for each query.
Solution Approach 2:
The system creates a simplified copy of the LLM's generation process through a generative model that replicates hallucination behaviors. This model copy can be trained separately using simulation algorithms and used to predict hallucination probabilities without requiring actual LLM generation for every query, thus reducing computational waste.
2Measurement precision
If conventional post-generation analysis methods are used to detect hallucinations, then hallucination detection is possible after output generation, but computational resources are already consumed and users cannot prevent hallucinatory generations
Solution Approach 1:
The system shifts from post-generation analysis to pre-generation prediction by training the generative model to assess hallucination probabilities before queries are processed. This allows users to identify and revise high-risk queries before they consume computational resources, eliminating the need for time-consuming post-generation analysis.
Solution Approach 2:
The system implements feedback by using simulation algorithms to generate labeled training data with known hallucination outcomes. This feedback loop allows the generative model to learn from simulated examples and improve its prediction accuracy, enabling it to provide reliable hallucination probability assessments for real queries.
3Ease of operation
If LLMs operate without query quality assessment, then all queries are processed equally, but users lack accountability for submitting queries prone to hallucination
Solution Approach 1:
The system applies a metaphorical 'color change' by providing visual or numerical indicators (probability scores) that reveal the hallucination risk level of each query. This makes the previously invisible risk visible, allowing users to assess query quality and take accountability without complicating the basic query submission process.
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for predicting probability of hallucination before generation for a query imposed to a Large Language Model (LLM) are disclosed. A processor causes a trained generative model to receive a query from a user via a user interface operatively connected to the generative model; perturbs the received query n times into unique variations that retain the original semantic meaning of the received query yet significantly diverge lexically; implements n+1 independent agents to sample an output from each query including the original received query; applies the simulation algorithm on the sampled outputs; derives an empirical estimate into an expected rate of hallucination for the original received query as a ground truth for the encoder; and outputs a probability of hallucination value for the query received by the generative model before the LLM generates an output.