Inline Flag-Token Prompts for Reliable LLM Fraud Detection
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Solution Overview
Problem
Existing anti-fraud systems relying on generative language models are less reliable and costly, lacking specific knowledge of online fraud and requiring substantial computational resources and expertise, while conventional methods are cumbersome for ordinary users.
Innovation Solution
A chatbot system with a prompt manager dynamically updates a generative language model prompt inline, using flag tokens to trigger code snippets for fraud detection, allowing efficient and user-friendly fraud detection without re-initializing the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a pre-trained generative language model is used for fraud detection, then the system becomes more accessible and easier to operate, but the reliability and accuracy of fraud detection decreases due to lack of specific fraud knowledge
Solution Approach 1:
The patent implements dynamic prompt updating during the inference process. The prompt manager monitors the language model's outputs and dynamically modifies the prompt based on detected patterns and flag tokens, allowing the system to adapt to different fraud scenarios without retraining the model. This dynamic adjustment enables the pre-trained model to achieve reliable fraud detection results while maintaining ease of operation.
Solution Approach 2:
The system changes the parameters of the language model by dynamically modifying the prompt text and configuration parameters during inference. The prompt manager adjusts prompt parameters such as temperature, top-p sampling, and prompt structure based on the detected fraud patterns, enabling the same pre-trained model to reliably detect different types of fraud without requiring model retraining.
2Measurement precision
If a custom language model trained for fraud detection is developed, then the detection accuracy improves, but the computational cost and expertise requirements increase significantly
Solution Approach 1:
The patent performs preliminary actions by using a pre-trained language model that has already been trained on general language data. Instead of training a new model from scratch, the system prepares the model in advance with general language capabilities and then uses dynamic prompting to adapt it to fraud detection tasks, significantly reducing computational costs while maintaining detection accuracy.
Solution Approach 2:
The system uses a copy of a pre-trained language model rather than developing a completely new model. The prompt manager creates and modifies prompt templates that guide the pre-trained model's behavior for fraud detection, avoiding the need to train a custom model while achieving comparable accuracy to custom-trained models.
3Measurement precision
If the language model prompt is updated dynamically during inference, then the fraud detection accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the fraud detection system into distinct functional components: the language model for generating predictions, the prompt manager for dynamic prompt updates, and the code snippet executor for implementing detection logic. This segmentation allows each component to remain relatively simple while the overall system achieves high detection accuracy through coordinated interaction between components.
Solution Approach 2:
The prompt manager acts as an intermediary between the language model and the fraud detection logic. It receives the model's outputs, interprets flag tokens, executes appropriate code snippets, and dynamically updates prompts based on the results. This intermediary layer simplifies the overall system architecture by centralizing the complexity of dynamic prompt management in a single modular component.
4Adaptability or versatility
If flag tokens and code snippets are used to update prompts inline, then the system adapts to changing fraud patterns, but the prompt management complexity increases
Solution Approach 1:
The system implements self-service by enabling the prompt manager to automatically detect fraud patterns through flag tokens in the language model's output and autonomously update prompts without human intervention. The code snippets are automatically executed based on detected patterns, allowing the system to adapt to changing fraud patterns while keeping prompt management complexity manageable through automation.
Solution Approach 2:
The system uses feedback mechanisms where the language model's outputs containing flag tokens provide information back to the prompt manager. The prompt manager uses this feedback to determine which code snippets to execute and how to update the prompt, creating a closed-loop system that adapts to fraud patterns while managing complexity through structured feedback processing.
Data Source
AI summary
In some embodiments, a security chatbot comprises a generative language module (GLM) and a prompt manager configured to dynamically update the GLM input prompt according to the output of the GLM. The input prompt instructs the GLM to carry out a task (e.g., determine whether a target message received by a user is indicative of online fraud) and in response, to output a specific flag token. In response to detecting the flag token within the output of the GLM, the prompt manager may selectively execute a code snippet identified according to the respective flag token. Executing the code snippet effectively updates the prompt, which is then fed back to the GLM for the next inference step. In contrast to conventional LLM prompting, updates to the prompt herein comprise modifications (e.g., insertion of other flag tokens, etc.) other than merely appending an inferred token to the previous prompt. Furthermore, the prompt modifications are performed inline, i.e., without submitting an entirely new prompt to the GLM.