A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models (2024)

Abstract: The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks. However, the proliferation of FMs brings forth a critical challenge: the potential to generate hallucinated outputs, particularly in high-stakes applications. The tendency of foundation models to produce hallucinated content arguably represents the biggest hindrance to their widespread adoption in real-world scenarios, especially in domains where reliability and accuracy are paramount. This survey paper presents a comprehensive overview of recent developments that aim to identify and mitigate the problem of hallucination in FMs, spanning text, image, video, and audio modalities. By synthesizing recent advancements in detecting and mitigating hallucination across various modalities, the paper aims to provide valuable insights for researchers, developers, and practitioners. Essentially, it establishes a clear framework encompassing definition, taxonomy, and detection strategies for addressing hallucination in multimodal foundation models, laying the foundation for future research and development in this pivotal area.

## introduction the rapid progress in large-scale foundation models (fms), spanning language, image, audio, and video domains, has revolutionized the field of artificial intelligence (ai). models such as gpt-3 @xcite , minigpt-4 @xcite , audiollm @xcite , and lav-ila @xcite have demonstrated remarkable abilities across diverse tasks, from text generation to multimodal understanding. as these models find wider applications in critical domains, there is a growing imperative to comprehend and alleviate their propensity to produce hallucinated outputs. ## hallucination in large language models despite the progress of llms, a notable challenge persists in their proneness to hallucinate, impeding their practical implementation. for instance, the illustration in figure 3 exemplifies the generated response by the llm, showing indications of hallucination. ## hallucination in large vision-language models large vision-language models (lvlms) have garnered significant attention in the ai community for their ability to handle visual and textual data simultaneously @xcite , @xcite , @xcite , @xcite , @xcite . nonetheless, similar to llms, lvlms also confront the issue of hallu-cination. figure 4 illustrates an example of visual hallucination. ## hallucinations in large video models large video models (lvms) represent a significant advancement, allowing for processing video data at scale. despite their potential for various applications like video understanding and generation, lvms face challenges with hallucinations, where misinterpretations of video frames can result in artificial or inaccurate visual data. figure 5 demonstrates the instances of hallucination observed in lvms. ## hallucinations in large audio models large audio models (lams) have emerged as a powerful tool in the realm of audio processing and generation, with a wide range of applications like speech recognition, music analysis, audio synthesis, and captioning @xcite , @xcite . while demonstrating remarkable capabilities, lams are susceptible to hallucinations -anomalies ranging from generating unrealistic audio by combining fabricated snippets to injecting false information like quotes or facts into summaries. additionally, they may fail to accurately capture the inherent features of audio signals, such as timbre, pitch, or background noise @xcite . figure 6 presents one example of audio hallucinations. ## future directions researchers are actively investigating techniques to mitigate hallucinations, which is crucial for sensitive applications @xcite , @xcite . the key future directions can include: data resources: fine-tuning carefully curated high-quality data, integrating structured knowledge from knowledge graphs, and employing task/domain-specific alignment techniques to enhance accuracy and relevance. automated evaluation: developing specialized metrics for factual accuracy and coherence, combining automated evaluation with human judgments, adversarial testing to identify weaknesses, and fine-tuning fact-checking datasets. improving detection and mitigation: leveraging reasoning mechanisms (e.g., chain of thought @xcite , tree of thought @xcite ), knowledge graph integration, specialized fact-checking models, bias mitigation techniques, active learning methodologies, and ethical guidelines/regulatory frameworks. multimodal hallucination: data-centric initiatives, cross-modal alignment, architectural innovations, standardized benchmarking, reframing hallucination as a feature, and enhancing interpretability and trust for reliable multimodal ai systems. ## conclusion this survey paper systematically categorizes existing research on hallucination within fms, providing comprehensive insights into critical aspects such as detection, mitigation, tasks, datasets, and evaluation metrics. it addresses the pressing issue of hallucination in fms, acknowledging its widespread impact across various domains. the paper underscores the importance of addressing this challenge by examining recent advancements in detection and mitigation techniques, given fms' indispensable role in critical tasks. its primary contribution is presenting a structured taxonomy for classifying hallucination in fms, spanning text, image, video, and audio domains.