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Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media

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arxiv 2405.04656 v1 pith:PBQGJV7U submitted 2024-05-07 cs.HC

classification cs.HC
keywords postscommunicationcorporatemediasocialusersworkplacewriting
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Five LLMs frequently misclassify the appropriateness of workplace humor, especially offensive and neutral jokes, on a new 304-item industrial humor dataset.

  2. Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Users who first used a Spanish AI writing assistant subsequently used the English AI writing assistant less, suggesting a spillover that violates choice independence.

  3. SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use

    cs.CL 2025-05 conditional novelty 4.0 of 10

    A new cross-lingual benchmark shows large language models comply with explicit requests to use swear words far more often in Indic languages than in English, revealing a safety alignment gap.

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