Auditing financial documents is a very tedious and time-consuming process. As
of today, it can already be simplified by employing AI-based solutions to
recommend relevant text passages from a report for each legal requirement of
rigorous accounting standards. However, these methods need to be fine-tuned
regularly, and they require abundant annotated data, which is often lacking in
industrial environments. Hence, we present ZeroShotALI, a novel recommender
system that leverages a state-of-the-art large language model (LLM) in
conjunction with a domain-specifically optimized transformer-based
text-matching solution. We find that a two-step approach of first retrieving a
number of best matching document sections per legal requirement with a custom
BERT-based model and second filtering these selections using an LLM yields
significant performance improvements over existing approaches.

零射击 ALI 是一种新颖的推荐系统，利用了最先进的大型语言模型（LLM）与领域特定优化的基于转换器的文本匹配解决方案，通过检索最佳匹配的文档段落并使用 LLM 进行筛选，显著改进了现有方法。

利用大型语言模型提高金融审计的零样本文本匹配

Improving Zero-Shot Text Matching for Financial Auditing with Large  Language Models

People now see social media sites as their sole source of information due to
their popularity. The Majority of people get their news through social media.
At the same time, fake news has grown exponentially on social media platforms
in recent years. Several artificial intelligence-based solutions for detecting
fake news have shown promising results. On the other hand, these detection
systems lack explanation capabilities, i.e., the ability to explain why they
made a prediction. This paper highlights the current state of the art in
explainable fake news detection. We discuss the pitfalls in the current
explainable AI-based fake news detection models and present our ongoing
research on multi-modal explainable fake news detection model.

人们现在认为社交媒体是他们唯一的信息来源，但同时社交媒体上假新闻也呈指数级增长。本文强调可解释的假新闻检测在当前技术水平上的现状，并介绍了我们正在开展的多模态可解释假新闻检测模型研究。

通过可解释的 AI 实现智能虚假新闻检测

Towards Smart Fake News Detection Through Explainable AI

Scientific research communities are embracing AI-based solutions to target
tractable scientific tasks and improve research workflows. However, the
development and evaluation of such solutions are scattered across multiple
disciplines. We formalize the problem of scientific AI benchmarking, and
propose a system called SAIBench in the hope of unifying the efforts and
enabling low-friction on-boarding of new disciplines. The system approaches
this goal with SAIL, a domain-specific language to decouple research problems,
AI models, ranking criteria, and software/hardware configuration into reusable
modules. We show that this approach is flexible and can adapt to problems, AI
models, and evaluation methods defined in different perspectives. The project
homepage is this https URL

该研究提出了一种名为 SAIBench 的系统，它使用专业语言 SAIL 分离科研问题，人工智能模型，排名标准和软硬件配置，以便在不同学科领域评估人工智能解决方案并提供低摩擦度入门。

SAIBench：科学人工智能基准测试

SAIBench: Benchmarking AI for Science

Machine learning abilities have become a vital component for various
solutions across industries, applications, and sectors. Many organizations seek
to leverage AI-based solutions across their business services to unlock better
efficiency and increase productivity. Problems, however, can arise if there is
a lack of quality data for AI-model training, scalability, and maintenance. We
propose a data-centric federated learning architecture leveraged by a public
blockchain and smart contracts to overcome this significant issue. Our proposed
solution provides a virtual public marketplace where developers, data
scientists, and AI-engineer can publish their models and collaboratively create
and access quality data for training. We enhance data quality and integrity
through an incentive mechanism that rewards contributors for data contribution
and verification. Those combined with the proposed framework helped increase
with only one user simulation the training dataset with an average of 100 input
daily and the model accuracy by approximately 4\%.

提出一种通过区块链和智能合约实现的数据中心联邦学习架构，其中包含一个虚拟的公共市场，提供高质量的数据以供训练，通过激励机制提高数据质量和完整性，该架构有助于提高训练数据集的增长速度和模型准确性。