Mitigating Hallucinations in Large Language Models using a Channel-Aware Domain-Adaptive Generative Adversarial Network (CADAGAN)

Abstract The increasing reliance on AI-driven text generation in domains requiring factual accuracy, such as healthcare, legal, and finance, has highlighted the critical issue of hallucination in generated content. CADAGAN, a novel Channel-Aware Domain-Adaptive Generative Adversarial Network, introduces an innovative framework that mitigates hallucinations through a combination of channel-aware processing and domain-adaptive learning, addressing the shortcomings of existing approaches. Modifying Llama, an open-source language model, CADAGAN incorporates a multi-channel generator and domain-adaptive discriminator to ensure the generation of factually consistent, fluent, and domain-relevant text. Extensive experiments across various domains demonstrated CADAGAN’s superior performance in reducing hallucinations, improving factual consistency, and maintaining linguistic coherence compared to baseline models. The system’s ability to dynamically adapt to new domains and correct errors in real-time significantly enhances its utility for knowledge-intensive applications, offering a reliable solution for environments where both fluency and factual correctness are paramount.