Towards reliable generative AI: A framework for addressing hallucination in generative models towards large language models

Registration ID: IJNRD_322316

Published ID: IJNRD2603487

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Utsha Sarker, Archy Biswas, Yubraj Kumar Rauniyar, Aman Singh, Lalit Vaishnav

- Large Language Models (LLMs) , Hallucination , Alignment ,Reinforcement Learning with Human Feedback Reinforcement Learning with Human Feedback (RLHF) , Constitutional AI , Self-Correction ,Generative AI Safety.

Abstract : Recent advances in big language models (LLMs), such as GPT-4 and Gemini, have led to the possibility of putting generative artificial intelligence (AI) to use in a myriad of its applications across all sectors of society. However, their tendency to produce hallucinate outputs, which are outputs that are plausible but wrong, and the concern over model misalignment, which is concerning given the potentially perilous impact of AI on our world, place serious limitations on their safe and reliable use, particularly in high risk areas such as healthcare, law and governance. These challenges include the probabilistic generation of the text in the face of uncertainty and the inclusion of imperfection with human-values and fact. In this paper, we propose a conceptual framework for trustworthy generative AI that has goals of reducing the hallucination and improving the alignment in LLMs. The framework integrates a variety of complementary techniques such as reinforcement learning with human feedback (RLHF), constitutional/self alignment, self correction mechanisms and post generation safety f ilter to offer a holistic approach to produce enhanced reliability. We make three significant contributions. First, we introduce the taxonomy of hallucinations and alignment strategies, collating the latest progress on the detection, mitigation and evaluation of the reliability of LLM. Second, we propose a unifyed multi layer framework, composed with training time alignment and inera time verification, to achieve a robust performance. Third, we illustrate evaluation dimensions and indicative evaluation metrics such as factual consistency, uncertainty calibration and abstention capability to evaluate trustworthiness in generative models in a systematic manner. The proposed framework is expected to lead to safer deployment of LLMs in such important critical areas as healthcare, legal systems, education, public governance, where reliability, transparency and consistency with human intent is essential to achieving responsible adoption of AI.

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Utsha Sarker, Archy Biswas, Yubraj Kumar Rauniyar, Aman Singh, & Lalit Vaishnav (March-2026). Towards reliable generative AI: A framework for addressing hallucination in generative models towards large language models. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), e705-e715. https://ijnrd.org/papers/IJNRD2603487.pdf

Volume 11 Issue 3, March-2026

Pages : e705-e715

Paper Reg. ID: IJNRD_322316

Published Paper Id: IJNRD2603487

Research Area: Other area not in list

Author Type: Indian Author

Country: Mohali, Punjab, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2603487.pdf

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