Summary of Benchmarking Floworks Against Openai & Anthropic: a Novel Framework For Enhanced Llm Function Calling, by Nirav Bhan et al.
Benchmarking Floworks against OpenAI & Anthropic: A Novel Framework for Enhanced LLM Function Calling
by Nirav Bhan, Shival Gupta, Sai Manaswini, Ritik Baba, Narun Yadav, Hillori Desai, Yash Choudhary, Aman Pawar, Sarthak Shrivastava, Sudipta Biswas
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces ThorV2, a novel architecture that enhances Large Language Models’ (LLMs) function calling abilities. The authors develop a comprehensive benchmark focused on HubSpot CRM operations to evaluate ThorV2 against leading models from OpenAI and Anthropic. Results show that ThorV2 outperforms existing models in accuracy, reliability, latency, and cost efficiency for both single and multi-API calling tasks. Additionally, ThorV2 is more reliable and scales better to multistep tasks compared to traditional models. This work offers the possibility of more accurate function-calling using smaller LLMs, with significant implications for AI assistants and broader applications in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for Large Language Models (LLMs) to do certain jobs. It’s called ThorV2, and it’s better than other ways at doing these tasks. The authors test ThorV2 against other models to see how well it works. They use a special benchmark focused on managing customer relationships using HubSpot CRM. The results show that ThorV2 is more accurate, reliable, and efficient than the other models. This new way of using LLMs could lead to better AI assistants and more practical uses in real-life situations. |
