Benchmarking Failures in Tool-Augmented Language Models

• URL: http://arxiv.org/abs/2503.14227v1
• Date: Tue, 18 Mar 2025 13:04:55 GMT
• Title: Benchmarking Failures in Tool-Augmented Language Models
• Authors: Eduardo Treviño, Hugo Contant, James Ngai, Graham Neubig, Zora Zhiruo Wang,
• Abstract summary: Tool-augmented language models (TaLMs) assume 'perfect' information access and tool availability, which may not hold in the real world.<n>We introduce the FAIL-TALMS benchmark, featuring two major failures: under-specified user queries and non-available tools.<n>We evaluate top-performing proprietary and open-source models, and find all current models except for Claude struggle to recognize missing tools or information.
• Score: 41.94295877935867
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The integration of tools has extended the capabilities of language models (LMs) beyond vanilla text generation to versatile scenarios. However, tool-augmented language models (TaLMs) often assume 'perfect' information access and tool availability, which may not hold in the real world. To systematically study TaLMs' imperfections, we introduce the FAIL-TALMS benchmark, featuring two major failures: under-specified user queries and non-available tools. FAIL-TALMS contains 1,749 examples using 906 tools across 21 categories, including single- and multi-tool usage. We evaluate top-performing proprietary and open-source models, and find all current models except for Claude struggle to recognize missing tools or information. Further, to study possible mitigation of the failures, we enable real-time human interaction, named the Ask-and-Help (AAH) method, to provide missing information or replace non-functional tools. While AAH can help models solve tasks more correctly when queries are under-specified, it brings minimal benefit when complex tools are broken.

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