Black-Box LLM Text Detection Using Distribution-Aligned Surrogates
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Solution Overview
Problem
Existing methods for detecting text generated by Large Language Models (LLMs) face challenges in adapting to new models and domains, particularly when using black-box LLMs, due to misalignment in probability curvature distributions and the need for tailored surrogate models.
Innovation Solution
A distribution-aligned LLM detection framework that fine-tunes surrogate models to align with target LLM distributions, using a logit-based approach to minimize the gap between probability curvatures, enabling effective detection without relying on logits from the source LLM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If zero-shot detectors use tailored surrogate models for each LLM, then detection accuracy is improved, but device complexity and adaptability worsen due to needing multiple models
Solution Approach 1:
The patent creates a universal surrogate model that can detect text from multiple different LLMs without requiring separate tailored models for each target model. This single surrogate model serves multiple functions across different LLM detections, reducing overall system complexity while maintaining detection accuracy through its ability to approximate various target model behaviors.
Solution Approach 2:
The patent modifies the surrogate model's parameters and training approach to make it adaptable to different target LLMs. By changing the training data and parameter configurations rather than creating entirely separate models, the system achieves accurate detection across multiple LLMs with a single flexible surrogate model structure.
2Measurement precision
If surrogate models are updated for new LLM versions, then detection accuracy is maintained, but loss of time and productivity worsen due to retraining requirements
Solution Approach 1:
The patent performs preliminary actions by creating a surrogate model with broad adaptability built-in from the start, rather than requiring updates for each new LLM version. The model is pre-configured to handle variations in LLM outputs through its training approach, reducing the need for frequent retraining and minimizing time loss when new models are released.
Solution Approach 2:
The patent makes the surrogate model dynamic and adaptable to new LLM versions through efficient retraining mechanisms. The model can be quickly updated with minimal training data when new LLMs are released, allowing it to maintain high detection accuracy without significant time investment in retraining compared to traditional tailored model approaches.
3Ease of operation
If black-box LLM detection is performed without logits, then ease of operation is improved, but measurement precision worsens due to distribution gaps
Solution Approach 1:
The patent introduces a surrogate model as an intermediary between the black-box LLM and the detection process. This intermediary approximates the target LLM's behavior and fills the distribution gap, enabling accurate detection of black-box LLM outputs without direct access to their internal logits or probability distributions.
Solution Approach 2:
The patent creates a copy (surrogate model) of the target LLM's behavior patterns and probability distributions. This copied model replicates the essential characteristics of the black-box LLM, allowing detection algorithms to operate on the surrogate's outputs rather than directly on the inaccessible black-box internals, thereby maintaining precision while improving ease of operation.
Data Source
AI summary
Systems and methods for detecting Large Language Model (LLM) generated text are provided. The systems and methods include sampling a text passage to generate alternative samples conditioned on the text passage based on a next token prediction in a surrogate LLM model and scoring a likelihood that the test passage sample is generated by an LLM model. The scoring includes a conditional probability which quantifies a distribution gap of a log of logits from the surrogate LLM model. The systems and methods further include comparing the scored text passage with a sample text generated in the surrogate LLM model trained to imitate a target LLM model. The comparison includes transforming the scores into a scaled representation and normalizing the scores.