The launch of ChatGPT by OpenAI in November 2022 marked a pivotal moment for
Artificial Intelligence, introducing Large Language Models (LLMs) to the
mainstream and setting new records in user adoption. LLMs, particularly
ChatGPT, trained on extensive internet data, demonstrate remarkable
conversational capabilities across various domains, suggesting a significant
impact on the workforce. However, these models are susceptible to errors -
"hallucinations" and omissions, generating incorrect or incomplete information.
This poses risks especially in contexts where accuracy is crucial, such as
legal compliance, medicine or fine-grained process frameworks.
There are both technical and human solutions to cope with this isse. This
paper explores the human factors that enable users to detect errors in LLM
outputs, a critical component in mitigating risks associated with their use in
professional settings. Understanding these factors is essential for
organizations aiming to leverage LLM technology efficiently, guiding targeted
training and deployment strategies to enhance error detection by users. This
approach not only aims to optimize the use of LLMs but also to prevent
potential downstream issues stemming from reliance on inaccurate model
responses. The research emphasizes the balance between technological
advancement and human insight in maximizing the benefits of LLMs while
minimizing the risks, particularly in areas where precision is paramount.
This paper performs a systematic literature research on this research topic,
analyses and synthesizes the findings, and outlines future research directions.
Literature selection cut-off date is January 11th 2024.

研究对 LLM 技术中的错误进行风险减轻，并分析和综合研究的结果及未来研究方向。

大型语言模型中的人为因素对错误检测的影响：系统文献综述与未来研究方向

The Human Factor in Detecting Errors of Large Language Models: A  Systematic Literature Review and Future Research Directions

This paper explores the intersection of Otome Culture and artificial
intelligence, particularly focusing on how Otome-oriented games fulfill the
emotional needs of young women. These games, which are deeply rooted in a
subcultural understanding of love, provide players with feelings of
satisfaction, companionship, and protection through carefully crafted narrative
structures and character development. With the proliferation of Large Language
Models (LLMs), there is an opportunity to transcend traditional static game
narratives and create dynamic, emotionally responsive interactions. We present
a case study of Tears of Themis, where we have integrated LLM technology to
enhance the interactive experience. Our approach involves augmenting existing
game narratives with a Question and Answer (QA) system, enriched through data
augmentation and emotional enhancement techniques, resulting in a chatbot that
offers realistic and supportive companionship.

该研究探讨了乙女文化和人工智能的交叉点，特别关注乙女导向游戏如何满足年轻女性的情感需求。这些游戏通过精心构建的叙事结构和角色发展，提供玩家满足感、陪伴感和保护感。通过整合大型语言模型（LLM）技术来增强互动体验，我们以《泪水之女神》为案例研究，将现有游戏叙事与问题 - 回答（QA）系统相结合，并通过数据增强和情感增强技术来丰富聊天机器人的功能，为玩家提供逼真且支持性的陪伴。

情感支持的富同理心用户导向的聊天机器人

An Empathetic User-Centric Chatbot for Emotional Support

With the continuous development and change exhibited by large language model
(LLM) technology, represented by generative pretrained transformers (GPTs),
many classic scenarios in various fields have re-emerged with new
opportunities. This paper takes ChatGPT as the modeling object, incorporates
LLM technology into the typical book resource understanding and recommendation
scenario for the first time, and puts it into practice. By building a
ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT,
this paper attempts to apply ChatGPT to recommendation modeling for three
typical tasks, book rating recommendation, user rating recommendation, and book
summary recommendation, and explores the feasibility of LLM technology in book
recommendation scenarios. At the same time, based on different evaluation
schemes for book recommendation tasks and the existing classic recommendation
models, this paper discusses the advantages and disadvantages of the BookGPT in
book recommendation scenarios and analyzes the opportunities and improvement
directions for subsequent LLMs in these scenarios.

本文介绍了一种基于 ChatGPT 的 BookGPT 书籍推荐系统，利用大型语言模型技术实现了书籍的评分、用户评分和书籍概述等三项典型任务。同时，本文还讨论了 BookGPT 在书籍推荐场景中的优缺点，并分析了后续 LLM 在这些场景中的机遇和改进方向。