User Prompt Validation Using Truthiness and Latent Sentiment
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Solution Overview
Problem
Existing technologies fail to identify and prevent incorrect outputs generated by Large Language Models (LLMs) due to input prompts with misaligned intentions or those lacking vulnerable keywords, leading to potential manipulation and misinterpretation.
Innovation Solution
A method and system for validating user input prompts using a set of validation databases to determine truthiness of language and latent sentiment, calculating a vulnerability score, and rendering a validation report.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional keyword detection technology is used to identify vulnerable input prompts, then prompts containing vulnerable keywords can be detected and restricted, but prompts without vulnerable keywords that still have misaligned intentions or wrong interpretations cannot be identified
Solution Approach 1:
The patent transitions from detecting only vulnerable keywords to analyzing multiple parameters including truthiness of language, latent sentiment, and coherence with the reason for the prompt. This multi-parameter approach enables detection of prompts without vulnerable keywords that still have misaligned intentions or wrong interpretations.
Solution Approach 2:
The patent introduces an intermediary validation system that uses validation databases and multiple analysis dimensions (truthiness, sentiment, coherence) to bridge the gap between keyword detection and comprehensive prompt validation, enabling identification of subtle manipulation attempts without vulnerable keywords.
2Productivity
If LLMs are given access to data and APIs to perform their functions, then they can provide useful information and services, but they become vulnerable to manipulation through injected input prompts
Solution Approach 1:
The patent applies preliminary validation actions before the LLM processes the input prompt. By validating the reason for the prompt, analyzing truthiness of language, assessing latent sentiment, and checking coherence beforehand, the system prevents manipulated prompts from reaching the LLM, thus protecting against harmful manipulation while preserving functional capability.
Solution Approach 2:
The patent implements a feedback mechanism where the validation system continuously monitors and evaluates input prompts using multiple criteria, and uses this feedback to determine whether to allow or restrict the prompt. This feedback loop enables the system to maintain security while preserving legitimate functionality.
3Object-affected harmful factors
If input prompts are restricted or censored based on vulnerable keywords, then security against known attacks is improved, but legitimate prompts that lack vulnerable keywords but have incorrect intentions are not filtered
Solution Approach 1:
The patent expands the validation parameters beyond vulnerable keywords to include truthiness of language, latent sentiment, and coherence with the reason for the prompt. This comprehensive parameter set enables accurate filtering of malicious prompts while preserving legitimate ones, improving both security and filtering accuracy simultaneously.
Data Source
AI summary
This disclosure relates to method and system for validating input prompt. The method includes receiving an input prompt and a reason for the input prompt from a User Interface (UI). The method further includes validating the reason for the input prompt using a set of validation databases. Upon successful validation of the reason for the input, the method further includes determining a truthiness of language and a latent sentiment corresponding to the input prompt based on predefined criteria. The method further includes calculating a vulnerability score corresponding to the input prompt based on the truthiness of language and the latent sentiment. The method further includes rendering a validation report for the input prompt on the UI.