One major issue highlighted by researchers at Princeton University in their analysis of AI agents is the lack of cost control in agent evaluations. Unlike benchmarking foundation models, evaluating AI agents can be much more expensive due to the use of stochastic language models that produce different results when given the same query multiple times. This variability in results often leads to agents generating several responses and using mechanisms like voting or external verification tools to choose the best answer, which can significantly increase computational costs.
In research settings, where the goal is to maximize accuracy, high inference costs may not be a significant concern. However, in practical applications, there is a limit to the budget available for each query, making it essential for agent evaluations to be cost-controlled. Failure to do so may result in the development of extremely costly agents simply to outperform others in benchmarks.
The researchers propose visualizing evaluation results as a Pareto curve of accuracy and inference cost and using techniques that jointly optimize the agent for these two metrics. By optimizing for both accuracy and cost, researchers and developers can create agents that are not only accurate but also cost-effective. This joint optimization approach enables a balance between optimizing the agent’s design and reducing variable costs, ultimately leading to more efficient AI agents.
Inference Costs for Real-World Applications
Another challenge highlighted by the researchers is the difference in evaluating models for research purposes versus developing downstream applications. In research, accuracy is often the primary focus, with inference costs being overlooked. However, when it comes to real-world applications, inference costs play a crucial role in determining which model and technique to use.
Evaluating inference costs for AI agents can be challenging due to varying pricing models from different providers and fluctuating costs of API calls. The researchers conducted a case study on NovelQA, a question-answering benchmark, and found that benchmarks meant for model evaluation may not accurately reflect costs in real-world applications. For example, retrieval-augmented generation (RAG) may appear worse than long-context models in a benchmark but may be more cost-effective in practice.
To address this issue, the researchers suggest adjusting model comparisons based on token pricing and creating holdout test sets that prevent agents from memorizing examples during training. By ensuring that benchmarks do not enable shortcuts or overfitting, developers can create more reliable evaluations for AI agents intended for real-world applications.
Preventing Overfitting and Shortcuts in AI Agents
Overfitting and shortcuts are significant challenges in evaluating AI agents, as they can lead to inflated accuracy estimates and unrealistic expectations of agent capabilities. Benchmark developers must create proper holdout test sets composed of examples that cannot be memorized during training to prevent agents from taking shortcuts, either intentionally or unintentionally.
The researchers emphasized the importance of creating different types of holdout samples based on the desired generality of the task the agent is designed to accomplish. By ensuring that benchmarks do not allow shortcuts, developers can establish more reliable evaluations for AI agents and prevent overfitting issues that may arise when agents find ways to cheat on benchmark tests.
In their analysis of 17 benchmarks, the researchers found that many lacked proper holdout datasets, which could enable agents to take shortcuts. They recommend that benchmark developers keep these test sets secret to prevent contamination or overfitting by language models. Additionally, benchmark developers should prioritize designing benchmarks that do not allow shortcuts, as it is much easier to prevent shortcuts at the benchmark level than to verify every individual agent for potential overfitting issues.
As AI agents continue to play a more significant role in everyday applications, the challenges of evaluating these agents for real-world use become increasingly apparent. With the need for cost control, accurate evaluation of inference costs, and prevention of overfitting and shortcuts, researchers and developers must adopt best practices to ensure that AI agents meet the demands of practical applications. By addressing these challenges, the development of AI agents for real-world use can be more reliable, cost-effective, and efficient.
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