Hallucinations in generative artificial intelligence and large language models: tests, datasets, detection and correction methods
TL;DR
This review paper provides a comprehensive overview of hallucinations in GAI and LLMs, and synthesizes a range of correction and mitigation techniques, from proactive measures during training to hybrid approaches that combine detection and intervention.
Abstract
Generative Artificial Intelligence (GAI) and Large Language Models (LLMs) have demonstrated significant capabilities in generating human-like content; however, they exhibit a propensity to fabricate spurious information, a phenomenon often termed hallucination. This review paper provides a comprehensive overview of hallucinations in GAI and LLMs. More specifically, this review encompasses their definitions, underlying mechanisms, taxonomies, commonly used tests, and datasets for evaluating hallucinations. In addition, this review dives into intrinsic and extrinsic factors contributing to these inaccuracies, including limitations in model architectures, training data biases, and inference algorithms, as well as examines various detection strategies [e.g., post-hoc consistency checks, external fact-checking, contrastive learning, uncertainty calibration methods, and Retrieval-Augmented Generation (RAG)]. The review also synthesizes a range of correction and mitigation techniques, from proactive measures during training to hybrid approaches that combine detection and intervention. Finally, this review integrates qualitative assessments and comparative insights to delineate the impact of hallucinations on user trust and acceptability, and to shed light on current challenges and future research trends.