AI Based Hallucination Detector

• Published in: International Research Journal on Advanced Science Hub Vol. 6; no. 12; pp. 381 - 389
• Main Authors: Ms. G. Illakiya, G.V. Jeeshitha, R. Manjushree, P. Harshini
• Format: Journal Article
• Language: English
• Published: 11.12.2024
• ISSN: 2582-4376; 2582-4376
• DOI: 10.47392/IRJASH.2024.050

The main goal of this project is to create an advanced system that can accept questions submitted by users, produce AI-generated answers, and guarantee their accuracy by using an integrated validation method. This involves connecting to external web APIs that have access to trustworthy and authoritative sources, allowing the system to compare AI-generated responses with verified factual data in real time. If the AI-generated answer is accurate, the system will show a confirmation, providing users with assurance of its reliability. However, if the answer is incorrect, the system will flag the error and present the accurate response, addressing the issue of AI generating believable but factually inaccurate answers. In addition, the system records inaccurate responses to detect recurring error trends, which helps to enhance and refine the AI model over time. It also includes an interactive explanation tool that allows users to comprehend the validation process, promoting transparency in decision- making. To increase user involvement, the system can provide information about the origin of the correct answer and offer insights into differences between the AI-generated answers and the correct ones. Additionally, the system will have real-time alerts for critical errors, promptly notifying users when high-risk or sensitive topics are involved. The system's overall accuracy will be evaluated through a periodic review mechanism, which will offer feedback on performance enhancements. Additionally, user feedback will be incorporated into the system to continuously improve it and adapt to changing information sources. Moreover, the system will utilize AI-based learning algorithms to anticipate and prevent potential errors, thus enhancing response quality over time. Ultimately, the project's goal is to establish a reliable and user-friendly AI environment that fosters trust through real-time verification, transparency, continuous enhancement, and minimized risks of incorrect AI outputs.