6 papers
Adobe Summit Concierge Evaluation with Human in the Loop
Yiru Chen, Sally Fang, Sai Sree Harsha +6
Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we pr…
Disambiguation in Conversational Question Answering in the Era of LLMs and Agents: A Survey
Md Mehrab Tanjim, Yeonjun In, Xiang Chen +8
Ambiguity remains a fundamental challenge in Natural Language Processing (NLP) due to the inherent complexity and flexibility of human language. With the advent of Large Language M…
ECLAIR: Enhanced Clarification for Interactive Responses in an Enterprise AI Assistant
John Murzaku, Zifan Liu, Vaishnavi Muppala +3
Large language models (LLMs) have shown remarkable progress in understanding and generating natural language across various applications. However, they often struggle with resolvin…
ECLAIR: Enhanced Clarification for Interactive Responses
John Murzaku, Zifan Liu, Md Mehrab Tanjim +3
We present ECLAIR (Enhanced CLArification for Interactive Responses), a novel unified and end-to-end framework for interactive disambiguation in enterprise AI assistants. ECLAIR ge…
Exploring Rewriting Approaches for Different Conversational Tasks
Md Mehrab Tanjim, Ryan A. Rossi, Mike Rimer +9
Conversational assistants often require a question rewriting algorithm that leverages a subset of past interactions to provide a more meaningful (accurate) answer to the user's que…
Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
Md Mehrab Tanjim, Xiang Chen, Victor S. Bursztyn +8
Multi-turn conversations with an Enterprise AI Assistant can be challenging due to conversational dependencies in questions, leading to ambiguities and errors. To address this, we…