Factual Text

Factual text generation focuses on creating accurate and reliable text from structured data or knowledge bases, addressing challenges like ensuring factual consistency and avoiding hallucinations. Current research emphasizes improving the ability of large language models to learn and utilize true factual associations rather than relying on superficial word co-occurrences, often employing contrastive learning, reinforcement learning, and retrieval-augmented generation techniques. This area is crucial for advancing applications such as question answering, recommendation systems, and cross-lingual information access, particularly in low-resource language settings, by enabling the creation of more informative and trustworthy text-based outputs.