The Necessity of a Unified Framework for LLM-Based Agent Evaluation
This addresses the need for fair and reproducible evaluation in the field of LLM-based agents, but it is incremental as it focuses on standardization rather than a new method.
The paper tackles the problem of evaluating LLM-based agents, noting that current benchmarks are confounded by factors like prompts and tool configurations, leading to unfair and non-reproducible results, and proposes a unified framework to standardize evaluation.
With the advent of Large Language Models (LLMs), general-purpose agents have seen fundamental advancements. However, evaluating these agents presents unique challenges that distinguish them from static QA benchmarks. We observe that current agent benchmarks are heavily confounded by extraneous factors, including system prompts, toolset configurations, and environmental dynamics. Existing evaluations often rely on fragmented, researcher-specific frameworks where the prompt engineering for reasoning and tool usage varies significantly, making it difficult to attribute performance gains to the model itself. Additionally, the lack of standardized environmental data leads to untraceable errors and non-reproducible results. This lack of standardization introduces substantial unfairness and opacity into the field. We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation. To this end, we introduce a proposal aimed at standardizing agent evaluation.