Recent studies highlight the reliance of Large Language Models (LLMs) on high-quality, diverse data for optimal performance. The data sourced from the Internet often aggregated into datasets like the Common Crawl corpus, presents significant quality variability and necessitates extensive cleaning. Moreover, specific domain knowledge is usually presented in HTML, but there is a lack of effective methods to clean them into the training corpus automatically. Traditional cleaning methods involve either labor-intensive human teams that lack scalability or static heuristics that lead to suboptimal outcomes and are unable to be applied to specific target domains. In this paper, inspired by the recent progress in employing LLMs as versatile agents for diverse tasks, we take the initiative to explore the potential of these agents in automating data-cleaning methodologies. By configuring LLMs as an agent team that imitates the human data-cleaning team, we can automatically generate cleaning rules that traditionally require the involvement of data-cleaning experts. These rules are developed using a limited number of data samples and can then be applied broadly to substantial portions of raw data from the same domain. We demonstrate the efficiency and effectiveness of on both pre-train scale corpora such as Common Crawl and specific target websites. Both automatic and human evaluations of the quality of the cleaned content highlight the feasibility of using LLMs to prepare their training corpus.

Although many large-scale knowledge bases (KBs) claim to contain multilingual information, their support for many non-English languages is often incomplete. This incompleteness gives birth to the task of cross-lingual question answering over knowledge base (xKBQA), which aims to answer questions in languages different from that of the provided KB. One of the major challenges facing xKBQA is the high cost of data annotation, leading to limited resources available for further exploration. Another challenge is mapping KB schemas and natural language expressions in the questions under cross-lingual settings. In this paper, we propose a novel approach for xKBQA in a reading comprehension paradigm. We convert KB subgraphs into passages to narrow the gap between KB schemas and questions, which enables our model to benefit from recent advances in multilingual pre-trained language models (MPLMs) and cross-lingual machine reading comprehension (xMRC). Specifically, we use MPLMs, with considerable knowledge of cross-lingual mappings, for cross-lingual reading comprehension. Existing high-quality xMRC datasets can be further utilized to finetune our model, greatly alleviating the data scarcity issue in xKBQA. Extensive experiments on two xKBQA datasets in 12 languages show that our approach outperforms various baselines and achieves strong few-shot and zero-shot performance. Our dataset and code are released for further research.