With the introduction of ChatGPT, OpenAI made large language models (LLM)
accessible to users with limited IT expertise. However, users with no
background in natural language processing (NLP) might lack a proper
understanding of LLMs. Thus the awareness of their inherent limitations, and
therefore will take the systems' output at face value. In this paper, we
systematically analyse prompts and the generated responses to identify possible
problematic issues with a special focus on gender biases, which users need to
be aware of when processing the system's output. We explore how ChatGPT reacts
in English and German if prompted to answer from a female, male, or neutral
perspective. In an in-depth investigation, we examine selected prompts and
analyse to what extent responses differ if the system is prompted several times
in an identical way. On this basis, we show that ChatGPT is indeed useful for
helping non-IT users draft texts for their daily work. However, it is
absolutely crucial to thoroughly check the system's responses for biases as
well as for syntactic and grammatical mistakes.

分析 ChatGPT 对有性别偏见和提示分析的系统回应，检查其可用性和限制。

ChatGPT 中的性别偏见有多普遍？-- 探索德语和英语 ChatGPT 的回答

How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English  ChatGPT Responses

We present a novel end-to-end trainable neural network model for
task-oriented dialog systems. The model is able to track dialog state, issue
API calls to knowledge base (KB), and incorporate structured KB query results
into system responses to successfully complete task-oriented dialogs. The
proposed model produces well-structured system responses by jointly learning
belief tracking and KB result processing conditioning on the dialog history. We
evaluate the model in a restaurant search domain using a dataset that is
converted from the second Dialog State Tracking Challenge (DSTC2) corpus.
Experiment results show that the proposed model can robustly track dialog state
given the dialog history. Moreover, our model demonstrates promising results in
producing appropriate system responses, outperforming prior end-to-end
trainable neural network models using per-response accuracy evaluation metrics.

本文提出了一种新颖的端到端可训练的神经网络模型，用于处理任务导向的对话系统，该模型将对话状态、API 调用和结构化的知识库查询结果相结合，以成功完成任务导向的对话，并且能够以联合学习为基础，更好地处理对话历史，生成结构良好的系统响应。在餐厅搜索领域的实验结果表明，相比先前的端到端可训练的神经网络模型，我们提出的模型在适当的系统响应生成方面表现更为卓越。