ToolTalk: Evaluating Tool-Usage in a Conversational Setting

Large language models (LLMs) have displayed massive improvements in reasoning 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 requiring multi-step tool usage specified through dialogue. ToolTalk contains 28 tools grouped into 7 plugins, and includes a complete simulated implementation 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. Large language models (LLMs) can perform impressive feats in natural language understanding, generation, and other tasks involving manipulation of text. With appropriate adjustments after pretraining, they can hold fluent and natural conversations with users. However, the scope of such conversations is still limited by LLMs lacking access to knowledge outside of their training data, exhibiting limited mathematical reasoning and computational abilities, and otherwise being unable to interact with the outside world. To overcome these limitations, various prior works have proposed integrating LLM-powered chatbots with the ability to use tools such as search engines (Nakano et al., 2022), calculators, or web APIs (Mialon et al., 2023).