AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed
for human-AI collaboration research. Its standout feature is the ability for
researchers to create customized AI personas for diverse experimental setups
using a user-friendly interface. This functionality is essential for simulating
various interpersonal dynamics in team settings. AI Collaborator excels in
mimicking different team behaviors, enabled by its advanced memory system and a
sophisticated personality framework. Researchers can tailor AI personas along a
spectrum from dominant to cooperative, enhancing the study of their impact on
team processes. The tool's modular design facilitates integration with digital
platforms like Slack, making it versatile for various research scenarios. AI
Collaborator is thus a crucial resource for exploring human-AI team dynamics
more profoundly.

AI Collaborator 使用人性化界面，在人工智能与人类合作研究中，能够创建个性化的 AI 形象，并模拟各种团队合作中的人际关系，从而深入研究其对团队过程的影响。

AI 协作伙伴：在教育和职业环境中搭建人机交互的桥梁

The AI Collaborator: Bridging Human-AI Interaction in Educational and  Professional Settings

Gene set analysis is a mainstay of functional genomics, but it relies on
manually curated databases of gene functions that are incomplete and unaware of
biological context. Here we evaluate the ability of OpenAI's GPT-4, a Large
Language Model (LLM), to develop hypotheses about common gene functions from
its embedded biomedical knowledge. We created a GPT-4 pipeline to label gene
sets with names that summarize their consensus functions, substantiated by
analysis text and citations. Benchmarking against named gene sets in the Gene
Ontology, GPT-4 generated very similar names in 50% of cases, while in most
remaining cases it recovered the name of a more general concept. In gene sets
discovered in 'omics data, GPT-4 names were more informative than gene set
enrichment, with supporting statements and citations that largely verified in
human review. The ability to rapidly synthesize common gene functions positions
LLMs as valuable functional genomics assistants.

OpenAI 的 GPT-4 是一个基于生物医学知识的大型语言模型，能够从嵌入式知识中提出关于普遍基因功能的假设，为基因集分析提供可靠的功能概述和支持。

评估大型语言模型在基因集功能发现中的应用

Evaluation of large language models for discovery of gene set function

This study investigates the consistency of feedback ratings generated by
OpenAI's GPT-4, a state-of-the-art artificial intelligence language model,
across multiple iterations, time spans and stylistic variations. The model
rated responses to tasks within the Higher Education (HE) subject domain of
macroeconomics in terms of their content and style. Statistical analysis was
conducted in order to learn more about the interrater reliability, consistency
of the ratings across iterations and the correlation between ratings in terms
of content and style. The results revealed a high interrater reliability with
ICC scores ranging between 0.94 and 0.99 for different timespans, suggesting
that GPT-4 is capable of generating consistent ratings across repetitions with
a clear prompt. Style and content ratings show a high correlation of 0.87. When
applying a non-adequate style the average content ratings remained constant,
while style ratings decreased, which indicates that the large language model
(LLM) effectively distinguishes between these two criteria during evaluation.
The prompt used in this study is furthermore presented and explained. Further
research is necessary to assess the robustness and reliability of AI models in
various use cases.

本研究探讨了 OpenAI 的 GPT-4 在多次迭代、时间跨度和风格变化中生成的反馈评分的一致性。通过对高等教育领域宏观经济学任务的回答进行评分，进行统计分析以了解评分的一致性、不同迭代之间的相关性以及内容和风格之间的相关性。结果显示，不同时间跨度的 ICC 得分介于 0.94 到 0.99 之间，表明 GPT-4 能够在有明确提示的情况下生成一致的评分。内容和风格评分之间的相关性为 0.87。使用不恰当的风格时，平均内容评分保持不变，而风格评分下降，这表明大型语言模型在评估过程中有效区分了这两个标准。本研究还介绍和解释了所使用的提示。需要进一步研究以评估 AI 模型在各种应用场景中的稳健性和可靠性。