Benchmarking Floworks against OpenAI & Anthropic: A Novel Framework for Enhanced LLM Function Calling
This work addresses the problem of enhancing LLM function calling for AI assistants and real-world applications, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the challenge of limited economic impact due to LLMs' tool use and function calling by introducing ThorV2, a novel architecture that outperforms leading models from OpenAI and Anthropic in accuracy, reliability, latency, and cost efficiency for CRM operations.
Large Language Models (LLMs) have shown remarkable capabilities in various domains, yet their economic impact has been limited by challenges in tool use and function calling. This paper introduces ThorV2, a novel architecture that significantly enhances LLMs' function calling abilities. We develop a comprehensive benchmark focused on HubSpot CRM operations to evaluate ThorV2 against leading models from OpenAI and Anthropic. Our results demonstrate that ThorV2 outperforms existing models in accuracy, reliability, latency, and cost efficiency for both single and multi-API calling tasks. We also show that ThorV2 is far more reliable and scales better to multistep tasks compared to traditional models. Our work offers the tantalizing possibility of more accurate function-calling compared to today's best-performing models using significantly smaller LLMs. These advancements have significant implications for the development of more capable AI assistants and the broader application of LLMs in real-world scenarios.