Since OpenAI's release of ChatGPT, generative AI has received significant
attention across various domains. These AI-based chat systems have the
potential to enhance the productivity of knowledge workers in diverse tasks.
However, the use of free public services poses a risk of data leakage, as
service providers may exploit user input for additional training and
optimization without clear boundaries. Even subscription-based alternatives
sometimes lack transparency in handling user data. To address these concerns
and enable Fraunhofer staff to leverage this technology while ensuring
confidentiality, we have designed and developed a customized chat AI called
FhGenie (genie being a reference to a helpful spirit). Within few days of its
release, thousands of Fraunhofer employees started using this service. As
pioneers in implementing such a system, many other organizations have followed
suit. Our solution builds upon commercial large language models (LLMs), which
we have carefully integrated into our system to meet our specific requirements
and compliance constraints, including confidentiality and GDPR. In this paper,
we share detailed insights into the architectural considerations, design,
implementation, and subsequent updates of FhGenie. Additionally, we discuss
challenges, observations, and the core lessons learned from its productive
usage.

自 OpenAI 发布 ChatGPT 以来，生成型人工智能在各个领域引起了很大的关注。这些基于人工智能的聊天系统有潜力提高知识工作者在各种任务中的生产力，然而，使用免费公共服务存在数据泄露的风险，因为服务提供商可能在没有明确边界的情况下利用用户输入进行额外的训练和优化，即使是基于订阅的替代方案在处理用户数据方面有时也缺乏透明度。为了解决这些问题并确保 Fraunhofer 员工能够利用这项技术同时保证机密性，我们设计和开发了一个定制的聊天人工智能系统，称为 FhGenie（genie 是一种指代有帮助精神的系统）。在其发布后的几天内，数千名 Fraunhofer 员工开始使用该服务。作为实施这样一个系统的先驱，许多其他组织也纷纷效仿。我们的解决方案借鉴了商业的大型语言模型（LLMs），并将其精心整合到我们的系统中，以满足我们的特定要求和合规约束，包括保密性和 GDPR。在本文中，我们分享了关于 FhGenie 的架构考虑、设计、实施和随后的更新的详细见解。此外，我们还讨论了从其生产使用中面临的挑战、观察和核心经验教训。

FhGenie：为企业和科学使用而定制的保密的聊天人工智能

FhGenie: A Custom, Confidentiality-preserving Chat AI for Corporate and  Scientific Use

One critical issue for chat systems is to stay consistent about preferences,
opinions, beliefs and facts of itself, which has been shown a difficult
problem. In this work, we study methods to assess and bolster utterance
consistency of chat systems. A dataset is first developed for studying the
inconsistencies, where inconsistent dialogue responses, explanations of the
inconsistencies, and recovery utterances are authored by annotators. This
covers the life span of inconsistencies, namely introduction, understanding,
and resolution. Building on this, we introduce a set of tasks centered on
dialogue consistency, specifically focused on its detection and resolution. Our
experimental findings indicate that our dataset significantly helps the
progress in identifying and resolving conversational inconsistencies, and
current popular large language models like ChatGPT which are good at resolving
inconsistencies however still struggle with detection.

研究论文强调了聊天系统的一致性问题，介绍了一个用于研究对话一致性的数据集和一系列任务，实验证明该数据集对于检测和解决会话中的不一致性有显著帮助，同时发现当前流行的大型语言模型如 ChatGPT 在解决不一致性方面表现良好但在检测方面仍存在困难。

不一致的对话回应及其恢复方法

Inconsistent dialogue responses and how to recover from them

Since DARPA Grand Challenges (rural) in 2004/05 and Urban Challenges in 2007,
autonomous driving has been the most active field of AI applications. Recently
powered by large language models (LLMs), chat systems, such as chatGPT and
PaLM, emerge and rapidly become a promising direction to achieve artificial
general intelligence (AGI) in natural language processing (NLP). There comes a
natural thinking that we could employ these abilities to reformulate autonomous
driving. By combining LLM with foundation models, it is possible to utilize the
human knowledge, commonsense and reasoning to rebuild autonomous driving
systems from the current long-tailed AI dilemma. In this paper, we investigate
the techniques of foundation models and LLMs applied for autonomous driving,
categorized as simulation, world model, data annotation and planning or E2E
solutions etc.

通过将大语言模型（LLMs）与基础模型结合，利用人类知识、常识和推理，重新构建现有的自动驾驶系统，以改变当前的长尾人工智能困境。