ToolTalk: Evaluating Tool-Usage in a Conversational Setting
Authors: name: Nicholas Farn, name: Richard Shin

Large language models (LLMs) have displayed massive improvements in reason-
ing and decision-making skills and can hold natural conversations with users.
Many recent works seek to augment LLM-based assistants with external tools so
they can access private or up-to-date information and carry out actions on
behalf of users. To better measure the performance of these assistants, this
paper introduces ToolTalk, a benchmark consisting of complex user intents re-
quiring multi-step tool usage specified through dialogue. ToolTalk contains 28
tools grouped into 7 plugins, and includes a complete simulated implementa-
tion of each tool, allowing for fully automated evaluation of assistants that
rely on execution feedback. ToolTalk also emphasizes tools that externally
affect the world rather than only tools for referencing or searching
information. We evaluate GPT-3.5 and GPT-4 on ToolTalk resulting in success
rates of 26% and 50% respectively. Our analysis of the errors reveals three
major categories and suggests some future directions for improvement. We
release ToolTalk at https://github.com/microsoft/ToolTalk.