PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Bach, Stephen H.; Sanh, Victor; Yong, Zheng-Xin; Webson, Albert; Raffel, Colin; Nayak, Nihal V.; Sharma, Abheesht; Kim, Tae-Woon; Saiful, Bari, M; Févry, Thibault; Alyafeai, Zaid; Dey, Manan; Santilli, Andrea; Sun, Zhiqing; Ben-David, Srulik; Xu, Canwen; Chhablani, Gunjan; Wang, Han; Fries, Jason; Al-shaibani, Maged S.; Sharma, Shanya; Thakker, Urmish; Almubarak, Khalid; Tang, Xiangru; Radev, Dragomir; Jiang, Mike Tian-Jian; Rush, Alexander M.

doi:10.48550/arxiv.2202.01279

Cited by 8 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Better prompting skills entail knowing how to ask questions to get desirable answers. Prompt engineering facilitates adaptation of GenAI to ad hoc tasks (Bach et al 2022) and has become a new skill for improving performance. Human agents who master prompting can know better what questions to ask to elicit customers' thinking about their problems and resulting feelings.…”

Section: Emotion Understanding Tenets For Practicementioning

confidence: 99%

The Caring Machine: Feeling AI for Customer Care

Huang,

Rust

2024

Journal of Marketing

View full text Add to dashboard Cite

Customer care is important for its role in relationship-building. This role has traditionally been performed by human customer agents, given the less mature feeling intelligence of AI. The emergence of interactive generative AI (GenAI) shows the potential for using AI for customer care in such emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this paper develops an AI-enabled customer care journey, beginning from accurate emotion recognition to empathetic response, emotional management support, and finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth top manager interviews and a CMO survey. By juxtaposing these requirements against the current feeling capabilities of GenAI, the technological challenges that need to be tackled by engineers are highlighted. This paper wraps up with a set of marketing tenets for implementing and researching the caring machine. These marketing tenets encompass verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to let customers reveal their thinking and feeling to enhance emotion understanding, employing “response engineering” for knowledge of customer preferences to personalize emotion management recommendation, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value.

show abstract

Section: Emotion Understanding Tenets For Practicementioning

confidence: 99%

The Caring Machine: Feeling AI for Customer Care

Huang,

Rust

2024

Journal of Marketing

View full text Add to dashboard Cite

show abstract

“…Some prompt engineering was inevitable through this process. 3,34,35 However, prompts were not designed to get a correct answer and some prompts (e.g., two atom harmonic oscillator) were never correctly completed. We do emphasize that the reported accuracy is not what one would expect of the rst prompt constructed on-the-y for a given problem.…”

Section: Methodsmentioning

confidence: 99%

Assessment of chemistry knowledge in large language models that generate code

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Some prompt engineering was inevitable through this process. 3,33,34 However, prompts were not designed to get a correct answer and some prompts (e.g., two atom harmonic oscillator) were never correctly completed. We do emphasize that the reported accuracy is not one would expect of the first prompt constructed on-the-fly for a given problem.…”

Section: Methodsmentioning

confidence: 99%

Assessment of chemistry knowledge in large language models that generate code

White

Hocky

Gandhi

et al. 2022

Preprint

View full text Add to dashboard Cite

In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we produce a benchmark set of problems, and evaluate these models based on correctness of code by automated testing and evaluation by experts. We find recent LLMs are able to write correct code across a variety of topics in chemistry and their accuracy can be increased by 30 percentage points via prompt engineering strategies, like putting copyright notices at the top of files. These dataset and evaluation tools are open source which can be contributed to or built upon by future researchers, and will serve as a community resource for evaluating the performance of new models as they emerge. We also describe some good practices for employing LLMs in chemistry. The general success of these models demonstrates that their impact on chemistry teaching and research is poised to be enormous.

show abstract

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

Cited by 8 publications

References 12 publications

The Caring Machine: Feeling AI for Customer Care

The Caring Machine: Feeling AI for Customer Care

Assessment of chemistry knowledge in large language models that generate code

Assessment of chemistry knowledge in large language models that generate code

Contact Info

Product

Resources

About