The Emotional Generation is a subset of emotional intelligence, which aims to output an emotional response based on emotional conditions as input. Emotion generation has a wide range of applications, including emotion chat, emotional visual caption, and emotional rewriting. However, it faces challenges such as a lack of interpretability and poor evaluability. In this paper, we propose the Emotional Chain-of-Thought (ECoT), a plug-and-play prompting method that enhances the performance of Large Language Models (LLMs) on various emotional generation tasks by aligning with human emotional intelligence guidelines. To assess the reliability of ECoT, we propose an automated model-based evaluation method called EGS. Extensive experimental results demonstrate the effectiveness of ECoT and EGS. Further,we discuss the promise of LLMs in the field of sentiment analysis and present key insights into the LLMs with the ECoT in emotional generation tasks.

My parents did not often concern themselves with my moral education, but, when they did, whatever wisdom or warnings they had to impart were accompanied by books--typically, pop-psychology best-sellers. Two stand out in my memory: "Reviving Ophelia: Saving the Selves of Adolescent Girls," by Mary Pipher, and "Emotional Intelligence," by Daniel Goleman. The first I read with great satisfaction. None of my transgressions were as alarming or exciting as what Pipher described--no drugs, no clandestine trips to the family liquor cabinet, no sullen application of nail polish--so her tales of bad behavior left me feeling both titillated and smug. The second book I set aside, as I suspected it had been purchased to point out my more common defects.

Claudio Fernández-Aráoz, senior edviser at Egon Zehnder International, analyzed cases where seemingly outstanding hires for C-level positions ended up being let go. His conclusion: they were hired for their business expertise and intelligence but fired for lapses in emotional intelligence. Some of the main questions companies (and HR departments especially) need to address are: Which employees do we need to retain? Who can lead us to excellence? These questions can only be answered based on high levels of uncertainty – which we must reduce as much as possible.

Hardly could anyone find a point at the Heidelberg Laurate Forum space-time without hearing about Artificial Intelligence, Machine Learning and Deep Learning. The truth is that the majority of mathematician lecturers were fascinated by the allure of algorithms. The most conservative ones have already transformed computing machines into self-developing systems functioning in a limited field of knowledge while some brave ones design broader AI applications. One of the few exceptions to the rule was Sir Michael Francis Atiyah. Defying the skepticism of his peers he claimed to have solved the Riemann Hypothesis which has remained unsolved for more than 160 years, making it a million dollar problem.

Editor's note: This is a guest post by Natalie Fratto, VP of Early Stage Practice at Silicon Valley bank. On the walk back from her high school, Max drops by the corner bodega to pick up a NeuroStim pill -- a prescription neuro-plasticity stimulator. Neurostim will accelerate her brain's ability to create new synaptic pathways, helping her quickly learn new behaviors and spot new connections when exposed to rapidly changing stimuli. The AEI is a standardized test, implemented 10 years ago in place of the SAT. It has become a globally accepted metric for aptitude and projected performance in the modern workplace.