@inproceedings{vigel-etal-2025-self,
title = "Self Knowledge-Tracing for Tool Use ({SKT}-Tool): Helping {LLM} Agents Understand Their Capabilities in Tool Use",
author = "Vigel, Joshua and
Cai, Renpei and
Chen, Eleanor and
Neema, Anish and
Liao, Austen and
Zhu, Kevin and
O{'}brien, Sean",
editor = "Drozd, Aleksandr and
Sedoc, Jo{\~a}o and
Tafreshi, Shabnam and
Akula, Arjun and
Shu, Raphael",
booktitle = "The Sixth Workshop on Insights from Negative Results in NLP",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.insights-1.14/",
doi = "10.18653/v1/2025.insights-1.14",
pages = "150--156",
ISBN = "979-8-89176-240-4",
abstract = "Large Language Models (LLMs) enhanced with tool use and APIs improve task performance but often misuse them, leading to inefficiency and unnecessary cost. We propose Self Knowledge-Tracing for Tool Use (SKT-Tool), a method enabling LLMs to assess their capabilities and make informed API usage decisions using knowledge tracing (KT). Our teacher-student framework helps LLMs optimize API calls in real-time without fine-tuning. Experiments across multiple datasets show that SKT-Tool significantly reduces API calls while maintaining accuracy, offering a scalable and cost-effective solution for tool-augmented LLMs. We conclude by analyzing shortcomings in this method and identifying directions for future work."
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<abstract>Large Language Models (LLMs) enhanced with tool use and APIs improve task performance but often misuse them, leading to inefficiency and unnecessary cost. We propose Self Knowledge-Tracing for Tool Use (SKT-Tool), a method enabling LLMs to assess their capabilities and make informed API usage decisions using knowledge tracing (KT). Our teacher-student framework helps LLMs optimize API calls in real-time without fine-tuning. Experiments across multiple datasets show that SKT-Tool significantly reduces API calls while maintaining accuracy, offering a scalable and cost-effective solution for tool-augmented LLMs. We conclude by analyzing shortcomings in this method and identifying directions for future work.</abstract>
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%0 Conference Proceedings
%T Self Knowledge-Tracing for Tool Use (SKT-Tool): Helping LLM Agents Understand Their Capabilities in Tool Use
%A Vigel, Joshua
%A Cai, Renpei
%A Chen, Eleanor
%A Neema, Anish
%A Liao, Austen
%A Zhu, Kevin
%A O’brien, Sean
%Y Drozd, Aleksandr
%Y Sedoc, João
%Y Tafreshi, Shabnam
%Y Akula, Arjun
%Y Shu, Raphael
%S The Sixth Workshop on Insights from Negative Results in NLP
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-240-4
%F vigel-etal-2025-self
%X Large Language Models (LLMs) enhanced with tool use and APIs improve task performance but often misuse them, leading to inefficiency and unnecessary cost. We propose Self Knowledge-Tracing for Tool Use (SKT-Tool), a method enabling LLMs to assess their capabilities and make informed API usage decisions using knowledge tracing (KT). Our teacher-student framework helps LLMs optimize API calls in real-time without fine-tuning. Experiments across multiple datasets show that SKT-Tool significantly reduces API calls while maintaining accuracy, offering a scalable and cost-effective solution for tool-augmented LLMs. We conclude by analyzing shortcomings in this method and identifying directions for future work.
%R 10.18653/v1/2025.insights-1.14
%U https://aclanthology.org/2025.insights-1.14/
%U https://doi.org/10.18653/v1/2025.insights-1.14
%P 150-156
Markdown (Informal)
[Self Knowledge-Tracing for Tool Use (SKT-Tool): Helping LLM Agents Understand Their Capabilities in Tool Use](https://aclanthology.org/2025.insights-1.14/) (Vigel et al., insights 2025)
ACL