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

arXiv: https://arxiv.org/abs/2405.04656 · HTML: https://mlsanity.com/paper/2405.04656/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2024.

## Built on by (most cited first, 14 of 14)

- [LaMP-QA: A Benchmark for Personalized Long-form Question Answering](https://mlsanity.com/paper/2506.00137/) (2025)
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- [Which Contributions Deserve Credit? Perceptions of Attribution in Human-AI Co-Creation](https://mlsanity.com/paper/2502.18357/) (2025)
- [Not All Jokes Land: Evaluating Large Language Models Understanding of Workplace Humor](https://mlsanity.com/paper/2506.01819/) (2025)
- [Learning from Natural Language Feedback for Personalized Question Answering](https://mlsanity.com/paper/2508.10695/) (2025)
- [Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages](https://mlsanity.com/paper/2502.09532/) (2025)
- [Iter-AHMCL: Alleviate Hallucination for Large Language Model via Iterative Model-level Contrastive Learning](https://mlsanity.com/paper/2410.12130/) (2024)
- [Harnessing the Potential of Large Language Models in Modern Marketing Management: Applications, Future Directions, and Strategic Recommendations](https://mlsanity.com/paper/2501.10685/) (2025)
- [Not My Truce: Personality Differences in AI-Mediated Workplace Negotiation](https://mlsanity.com/paper/2604.00464/) (2026)
- [Improving User Privacy in Personalized Generation: Client-Side Retrieval-Augmented Modification of Server-Side Generated Speculations](https://mlsanity.com/paper/2601.17569/) (2026)
- [PersonaMail: Learning and Adapting Personal Communication Preferences for Context-Aware Email Writing](https://mlsanity.com/paper/2602.17340/) (2026)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)

## Abstract (arXiv)

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.