ToolTalk: Evaluating Tool-Usage in a Conversational Setting
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.
655c157b1f8c93413b522a3b
Nicholas Farn
admin_assigned
2023-11-21T13:54:51.694Z
62f4193975277836ab9fd839
/avatars/20ea02d36b17895cba9a21c9ef1120e7.svg
Nicholas Farn
user
nifarn
655c157b1f8c93413b522a3c
Richard Shin
admin_assigned
2023-11-21T13:54:58.253Z
63cccaff6d080f9b7cfe9afb
/avatars/c1124d1ba5fd116788f944962700ba16.svg
Richard Shin
user
rshin
ToolTalk: Evaluating Tool-Usage in a Conversational Setting
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.
ToolTalk is a benchmark for evaluating LLM-based assistants using complex user intents requiring multi-step tool interactions, with success rates of 26% and 50% for GPT-3.5 and GPT-4 respectively.
Large language models LLMs ToolTalk benchmark user intents multi-step tool usage dialogue tools plugins simulated implementation execution feedback GPT-3.5 GPT-4