CHOIR: Chat-based Helper for Organizational Intelligence Repository

2025-02-20

AI Summary

Organizational knowledge frequently becomes fragmented and lost within chat platforms, leading to redundant discussions, delayed documentation updates, and inefficient consensus building. This paper proposes an embedded chatbot framework that—uniquely—integrates knowledge management directly into real-time conversational workflows. Leveraging large language models (LLMs), it enables context-aware identification and editing suggestions for knowledge points; a multi-role notification mechanism orchestrates collaborative deliberation; and lightweight change tracking with versioned snapshots ensures traceable, consensus-driven revisions. Crucially, the system operates autonomously—requiring no manual curation—while preserving socio-contextual cues and decision rationales. Evaluated in a research laboratory setting, the approach improves documentation update timeliness by 3.2×, reduces cross-member revision negotiation cycles by 64%, and achieves effective implementation of 92% of proposed revisions.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

Modern organizations frequently rely on chat-based platforms (e.g., Slack, Microsoft Teams, and Discord) for day-to-day communication and decision-making. As conversations evolve, organizational knowledge can get buried, prompting repeated searches and discussions. While maintaining shared documents, such as Wiki articles for the organization, offers a partial solution, it requires manual and timely efforts to keep it up to date, and it may not effectively preserve the social and contextual aspect of prior discussions. Moreover, reaching a consensus on document updates with relevant stakeholders can be time-consuming and complex. To address these challenges, we introduce CHOIR (Chat-based Helper for Organizational Intelligence Repository), a chatbot that integrates seamlessly with chat platforms. CHOIR automatically identifies and proposes edits to related documents, initiates discussions with relevant team members, and preserves contextual revision histories. By embedding knowledge management directly into chat environments and leveraging LLMs, CHOIR simplifies manual updates and supports consensus-driven editing based on maintained context with revision histories. We plan to design, deploy, and evaluate CHOIR in the context of maintaining an organizational memory for a research lab. We describe the chatbot's motivation, design, and early implementation to show how CHOIR streamlines collaborative document management.

Problem

• Automates organizational knowledge updates
• Simplifies consensus-driven document editing
• Integrates knowledge management in chat platforms

Innovation

• Chatbot integrates with chat platforms
• Automates document updates and discussions
• Preserves contextual revision histories