Authors: Shirley Kokane, Ming Zhu, Tulika Awalgaonkar, Jianguo Zhang, Thai Hoang, Akshara Prabhakar, Zuxin Liu, Tian Lan, Liangwei Yang, Juntao Tan, Rithesh Murthy, Weiran Yao, Zhiwei Liu, Juan Carlos Niebles, Huan Wang, Shelby Heinecke, Caiming Xiong, Silivo Savarese
Abstract: Evaluating the output of Large Language Models (LLMs) is one of the most
critical aspects of building a performant compound AI system. Since the output
from LLMs propagate to downstream steps, identifying LLM errors is crucial to
system performance. A common task for LLMs in AI systems is tool use. While
there are several benchmark environments for evaluating LLMs on this task, they
typically only give a success rate without any explanation of the failure
cases. To solve this problem, we introduce SpecTool, a new benchmark to
identify error patterns in LLM output on tool-use tasks. Our benchmark data set
comprises of queries from diverse environments that can be used to test for the
presence of seven newly characterized error patterns. Using SPECTOOL , we show
that even the most prominent LLMs exhibit these error patterns in their
outputs. Researchers can use the analysis and insights from SPECTOOL to guide
their error mitigation strategies.
Source: http://arxiv.org/abs/2411.13547v1
