  Attribution is a key concept in large language models (LLMs) as it enables
control over information sources and enhances the factuality of LLMs. While
existing approaches utilize open book question answering to improve
attribution, factual datasets may reward language models to recall facts that
they already know from their pretraining data, not attribution. In contrast,
counterfactual open book QA datasets would further improve attribution because
the answer could only be grounded in the given text. We propose Hallucination
Augmented Recitations (HAR) for creating counterfactual datasets by utilizing
hallucination in LLMs to improve attribution. For open book QA as a case study,
we demonstrate that models finetuned with our counterfactual datasets improve
text grounding, leading to better open book QA performance, with up to an 8.0%
increase in F1 score. Our counterfactual dataset leads to significantly better
performance than using humanannotated factual datasets, even with 4x smaller
datasets and 4x smaller models. We observe that improvements are consistent
across various model sizes and datasets, including multi-hop, biomedical, and
adversarial QA datasets.
