In the rapidly evolving world of artificial intelligence, Large Language Models (LLMs) are transcending their traditional roles in Natural Language Processing (NLP), showing remarkable capability in addressing real-world scenarios and applications. Observing this evolution, there arises an imperative to effectively evaluate these models, especially when they are deployed as agents in interactive and complex environments.
Stepping into this niche, researchers have brought forward AgentBench. This sophisticated multi-dimensional benchmark tool provides a comprehensive suite of eight meticulously designed environments. The objective? To rigorously assess and analyze an LLM’s intrinsic abilities in reasoning, problem-solving, and decision-making, especially in scenarios demanding open-ended responses and multi-turn dialogues.
A thorough examination of more than 25 distinct LLMs, spanning both commercial APIs and open-sourced models, has yielded insightful findings. The results underscore that while industry-leading commercial LLMs are proficient in navigating and acting as agents within intricate settings, there exists a clear performance chasm when compared to their open-sourced alternatives.
It’s worth noting that AgentBench isn’t a standalone endeavor. It represents a segment of a more expansive project that aspires for a holistic and systematic appraisal of Large Language Models. For professionals, researchers, or enthusiasts keen on accessing detailed resources, datasets, and bespoke evaluation methodologies, the AgentBench suite is conveniently hosted on github. The original research paper, offering a deeper dive into the subject, is available at arXiv:2308.03688.
