Fact-checking real-world claims often requires complex, multi-step reasoning
due to the absence of direct evidence to support or refute them. However,
existing fact-checking systems often lack transparency in their
decision-making, making it challenging for users to comprehend their reasoning
process. To address this, we propose the Question-guided Multi-hop
Fact-Checking (QACHECK) system, which guides the model's reasoning process by
asking a series of questions critical for verifying a claim. QACHECK has five
key modules: a claim verifier, a question generator, a question-answering
module, a QA validator, and a reasoner. Users can input a claim into QACHECK,
which then predicts its veracity and provides a comprehensive report detailing
its reasoning process, guided by a sequence of (question, answer) pairs.
QACHECK also provides the source of evidence supporting each question,
fostering a transparent, explainable, and user-friendly fact-checking process.
A recorded video of QACHECK is at this https URL

提出了问题引导的多跳事实核查（QACHECK）系统，它通过问一系列对验证主张至关重要的问题来指导模型的推理过程。QACHECK 具有五个关键模块：主张验证器，问题生成器，问答模块，问答校验器和推理器。用户可以将主张输入 QACHECK，它然后预测其真实性并提供一个全面的报告，详细介绍了其推理过程，由一系列的（问题，答案）对引导。QACHECK 还提供支持每个问题的证据来源，促进透明，可解释和用户友好的事实核查过程。

QACHECK: 一个针对问题引导的多跳事实核查的演示系统

QACHECK: A Demonstration System for Question-Guided Multi-Hop  Fact-Checking

Generative AI has made significant strides, yet concerns about the accuracy
and reliability of its outputs continue to grow. Such inaccuracies can have
serious consequences such as inaccurate decision-making, the spread of false
information, privacy violations, legal liabilities, and more. Although efforts
to address these risks are underway, including explainable AI and responsible
AI practices such as transparency, privacy protection, bias mitigation, and
social and environmental responsibility, misinformation caused by generative AI
will remain a significant challenge. We propose that verifying the outputs of
generative AI from a data management perspective is an emerging issue for
generative AI. This involves analyzing the underlying data from multi-modal
data lakes, including text files, tables, and knowledge graphs, and assessing
its quality and consistency. By doing so, we can establish a stronger
foundation for evaluating the outputs of generative AI models. Such an approach
can ensure the correctness of generative AI, promote transparency, and enable
decision-making with greater confidence. Our vision is to promote the
development of verifiable generative AI and contribute to a more trustworthy
and responsible use of AI.

通过从数据管理的角度分析多模态数据湖中的底层数据，包括文本文件、表格和知识图谱，并评估其质量和一致性，我们可以确保生成型人工智能的正确性，提升透明度，并更加自信地进行决策。我们的愿景是促进可验证的生成型人工智能的发展，为人工智能的可信和负责任使用做出贡献。