Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910438291144704 |
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| author | Lu, Zhuoran Mysore, Sheshera Safavi, Tara Neville, Jennifer Yang, Longqi Wan, Mengting |
| author_facet | Lu, Zhuoran Mysore, Sheshera Safavi, Tara Neville, Jennifer Yang, Longqi Wan, Mengting |
| contents | Workplace social media platforms enable employees to cultivate their professional image and connect with colleagues in a semi-formal environment. While semi-formal corporate communication poses a unique set of challenges, large language models (LLMs) have shown great promise in helping users draft and edit their social media posts. However, LLMs may fail to capture individualized tones and voices in such workplace use cases, as they often generate text using a "one-size-fits-all" approach that can be perceived as generic and bland. In this paper, we present Corporate Communication Companion (CCC), an LLM-empowered interactive system that helps people compose customized and individualized workplace social media posts. Using need-finding interviews to motivate our system design, CCC decomposes the writing process into two core functions, outline and edit: First, it suggests post outlines based on users' job status and previous posts, and next provides edits with attributions that users can contextually customize. We conducted a within-subjects user study asking participants both to write posts and evaluate posts written by others. The results show that CCC enhances users' writing experience, and audience members rate CCC-enhanced posts as higher quality than posts written using a non-customized writing assistant. We conclude by discussing the implications of LLM-empowered corporate communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04656 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media Lu, Zhuoran Mysore, Sheshera Safavi, Tara Neville, Jennifer Yang, Longqi Wan, Mengting Human-Computer Interaction Workplace social media platforms enable employees to cultivate their professional image and connect with colleagues in a semi-formal environment. While semi-formal corporate communication poses a unique set of challenges, large language models (LLMs) have shown great promise in helping users draft and edit their social media posts. However, LLMs may fail to capture individualized tones and voices in such workplace use cases, as they often generate text using a "one-size-fits-all" approach that can be perceived as generic and bland. In this paper, we present Corporate Communication Companion (CCC), an LLM-empowered interactive system that helps people compose customized and individualized workplace social media posts. Using need-finding interviews to motivate our system design, CCC decomposes the writing process into two core functions, outline and edit: First, it suggests post outlines based on users' job status and previous posts, and next provides edits with attributions that users can contextually customize. We conducted a within-subjects user study asking participants both to write posts and evaluate posts written by others. The results show that CCC enhances users' writing experience, and audience members rate CCC-enhanced posts as higher quality than posts written using a non-customized writing assistant. We conclude by discussing the implications of LLM-empowered corporate communication. |
| title | Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.04656 |