# Enhancing Discoverability in Enterprise Conversational Systems with Proactive Question Suggestions

arXiv: https://arxiv.org/abs/2412.10933 · HTML: https://mlsanity.com/paper/2412.10933/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 14 of 14)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion](https://mlsanity.com/paper/1507.02221/) (2015)
- [Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion](https://mlsanity.com/paper/2311.06318/) (2023)
- [Learning to Attend, Copy, and Generate for Session-Based Query Suggestion](https://mlsanity.com/paper/1708.03418/) (2017)
- [Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2402.04867/) (2024)
- [Evaluation and Continual Improvement for an Enterprise AI Assistant](https://mlsanity.com/paper/2407.12003/) (2024)
- [Attention-based Hierarchical Neural Query Suggestion](https://mlsanity.com/paper/1805.02816/) (2018)
- [Intent Models for Contextualising and Diversifying Query Suggestions](https://mlsanity.com/paper/1312.1611/) (2013)
- [Personalised Query Suggestion for Intranet Search with Temporal User Profiling](https://mlsanity.com/paper/1701.02050/) (2017)

## Built on by (most cited first, 2 of 2)

- [From Prompting to Alignment: A Generative Framework for Query Recommendation](https://mlsanity.com/paper/2504.10208/) (2025)
- [Quality Over Clicks: Iterative Reinforcement Learning for Early-Stage E-Commerce Query Suggestion](https://mlsanity.com/paper/2603.22922/) (2026)

## Abstract (arXiv)

Enterprise conversational AI systems are becoming increasingly popular to assist users in completing daily tasks such as those in marketing and customer management. However, new users often struggle to ask effective questions, especially in emerging systems with unfamiliar or evolving capabilities. This paper proposes a framework to enhance question suggestions in conversational enterprise AI systems by generating proactive, context-aware questions that try to address immediate user needs while improving feature discoverability. Our approach combines periodic user intent analysis at the population level with chat session-based question generation. We evaluate the framework using real-world data from the AI Assistant for Adobe Experience Platform (AEP), demonstrating the improved usefulness and system discoverability of the AI Assistant.