Factual Error Correction (FEC) aims to rectify false claims by making minimal revisions to align them more accurately with supporting evidence. However, the lack of datasets containing false claims and their corresponding corrections has impeded progress in this field. Existing distantly supervised models typically employ the mask-then-correct paradigm, where a masker identifies problematic spans in false claims, followed by a corrector to predict the masked portions. Unfortunately, accurately identifying errors in claims is challenging, leading to issues like over-erasure and incorrect masking. To overcome these challenges, we present PivotFEC, a method that enhances few-shot FEC with a pivot task approach using large language models (LLMs). Specifically, we introduce a pivot task called factual error injection, which leverages LLMs (e.g., ChatGPT) to intentionally generate text containing factual errors under few-shot settings; then, the generated text with factual errors can be used to train the FEC corrector. Our experiments on a public dataset demonstrate the effectiveness of PivotFEC in two significant ways: Firstly, it improves the widely-adopted SARI metrics by 11.3 compared to the best-performing distantly supervised methods. Secondly, it outperforms its few-shot counterpart (i.e., LLMs are directly used to solve FEC) by 7.9 points in SARI, validating the efficacy of our proposed pivot task.

The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent research has focused on obtaining query-informed document representations. During training, it expands the document with a real query, but during inference, it replaces the real query with a generated one. This inconsistency between training and inference causes the dense retrieval model to prioritize query information while disregarding the document when computing the document representation. Consequently, it performs even worse than the vanilla dense retrieval model because its performance heavily relies on the relevance between the generated queries and the real query. In this paper, we propose a curriculum sampling strategy that utilizes pseudo queries during training and progressively enhances the relevance between the generated query and the real query. By doing so, the retrieval model learns to extend its attention from the document alone to both the document and query, resulting in high-quality query-informed document representations. Experimental results on both in-domain and out-of-domain datasets demonstrate that our approach outperforms previous dense retrieval models.