Related papers: TAPS: Tool-Augmented Personalisation via Structured Tagging

TAPS: Tool-Augmented Personalisation via Structured Tagging

URL: http://arxiv.org/abs/2506.20409v2
Date: Thu, 26 Jun 2025 13:09:40 GMT
Title: TAPS: Tool-Augmented Personalisation via Structured Tagging
Authors: Ekaterina Taktasheva, Jeff Dalton,
Abstract summary: This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents.<n>We introduce TAPS, a novel solution that enhances personalised tool use by leveraging a structured tagging tool and an uncertainty-based tool detector.
Score: 0.7007504690449126
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Recent advancements in tool-augmented large language models have enabled them to interact with external tools, enhancing their ability to perform complex user tasks. However, existing approaches overlook the role of personalisation in guiding tool use. This work investigates how user preferences can be effectively integrated into goal-oriented dialogue agents. Through extensive analysis, we identify key weaknesses in the ability of LLMs to personalise tool use. To this end, we introduce TAPS, a novel solution that enhances personalised tool use by leveraging a structured tagging tool and an uncertainty-based tool detector. TAPS significantly improves the ability of LLMs to incorporate user preferences, achieving the new state-of-the-art for open source models on the NLSI task.

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