Conversational Assistants in Knowledge-Intensive Contexts: An Evaluation of LLM- versus Intent-based Systems
This addresses the problem of rigid conversational systems for knowledge workers, showing incremental benefits of using LLMs over traditional methods.
The study compared LLM-based and intent-based conversational assistants for knowledge management, finding that LLM-based systems had better user experience, task completion rate, usability, and perceived performance.
Conversational Assistants (CA) are increasingly supporting human workers in knowledge management. Traditionally, CAs respond in specific ways to predefined user intents and conversation patterns. However, this rigidness does not handle the diversity of natural language well. Recent advances in natural language processing, namely Large Language Models (LLMs), enable CAs to converse in a more flexible, human-like manner, extracting relevant information from texts and capturing information from expert humans but introducing new challenges such as ``hallucinations''. To assess the potential of using LLMs for knowledge management tasks, we conducted a user study comparing an LLM-based CA to an intent-based system regarding interaction efficiency, user experience, workload, and usability. This revealed that LLM-based CAs exhibited better user experience, task completion rate, usability, and perceived performance than intent-based systems, suggesting that switching NLP techniques can be beneficial in the context of knowledge management.