Challenges in Evaluating Agent Performance: A Critical Analysis
Snippet
"6.2 Challenges with Agent Evaluation
While LLMs are evaluated on a standard set of benchmarks designed to gauge their general understanding and reasoning capabilities, the benchmarks for agent evaluation vary greatly.
Many research teams introduce their own unique agent benchmarks alongside their agent implementation which makes comparing multiple agent implementations on the same benchmark challenging. Additionally, many of these new agent-specific benchmarks include a hand-crafted, highly complex, evaluation set where the results are manually scored. This can provide a high-quality assessment of a method’s capabilities, but it also lacks the robustness of a larger dataset and risks introducing bias into the evaluation, since the ones developing the method are also the ones writing and scoring the results. Agents can also have problems generating a consistent answer over multiple iterations, due to variability in the models, environment, or problem state. This added randomness poses a much larger problem to smaller, complex evaluation sets.
6.3 Impact of Data Contamination and Static Benchmarks
Some researchers evaluate their agent implementations on the typical LLM benchmarks. Emerging research indicates that there is significant data contamination in the model’s training data, supported by the observation that a model’s performance significantly worsens when benchmark questions are modified. This raises doubts on the authenticity of benchmark scores for both the language models and language model powered agents.
Furthermore, researchers have found that “As LLMs progress at a rapid pace, existing datasets usually fail to match the models’ ever-evolving capabilities, because the complexity level of existing benchmarks is usually static and fixed”. To address this, work has been done to create dynamic benchmarks that are resistant to simple memorization. Researchers have also explored the idea of generating an entirely synthetic benchmark based on a user’s specific environment or use case. While these techniques can help with contamination, decreasing the level of human involvement can pose additional risks regarding correctness and the ability to solve problems.
6.4 Benchmark Scope and Transferability
Many language model benchmarks are designed to be solved in a single iteration, with no tool calls, such as MMLU or GSM8K. While these are important for measuring the abilities of base language models, they are not good proxies for agent capabilities because they do not account for agent systems’ ability to reason over multiple steps or access outside information. StrategyQA improves upon this by assessing models’ reasoning abilities over multiple steps, but the answers are limited to Yes/No responses. As the industry continues to pivot towards agent focused use-cases additional measures will be needed to better assess the performance and generalizability of agents to tasks involving tools that extend beyond their training data.
Some agent specific benchmarks like AgentBench evaluate language model-based agents in a variety of different environments such as web browsing, command-line interfaces, and video games. This provides a better indication for how well agents can generalize to new environments, by reasoning, planning, and calling tools to achieve a given task. Benchmarks like AgentBench and SmartPlay introduce objective evaluation metrics designed to evaluate the implementation’s success rate, output similarity to human responses, and overall efficiency. While these objective metrics are important to understanding the overall reliability and accuracy of the implementation, it is also important to consider more nuanced or subjective measures of performance. Metrics such as efficiency of tool use, reliability, and robustness of planning are nearly as important as success rate but are much more difficult to measure. Many of these metrics require evaluation by a human expert, which can be costly and time consuming compared to LLM-as-judge evaluations.
6.5 Real-world Applicability
Many of the existing benchmarks focus on the ability of Agent systems to reason over logic puzzles or video games. While evaluating performance on these types of tasks can help get a sense of the reasoning capabilities of agent systems, it is unclear whether performance on these benchmarks translates to real-world performance. Specifically, real-world data can be noisy and cover a much wider breadth of topics that many common benchmarks lack.
One popular benchmark that uses real-world data is WildBench, which is sourced from the WildChat dataset of 570,000 real conversations with ChatGPT. Because of this, it covers a huge breadth of tasks and prompts. While WildBench covers a wide range of topics, most other real-world benchmarks focus on a specific task. For example, SWE-bench is a benchmark that uses a set of real-world issues raised on GitHub for software engineering tasks in Python. This can be very helpful when evaluating agents designed to write Python code and provides a sense for how well agents can reason about code related problems; however, it is less informative when trying to understand agent capabilities involving other programming languages.
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Challenges in Evaluating Agent Performance: A Critical Analysis
Snippet
"6.2 Challenges with Agent Evaluation
While LLMs are evaluated on a standard set of benchmarks designed to gauge their general understanding and reasoning capabilities, the benchmarks for agent evaluation vary greatly.
Many research teams introduce their own unique agent benchmarks alongside their agent implementation which makes comparing multiple agent implementations on the same benchmark challenging. Additionally, many of these new agent-specific benchmarks include a hand-crafted, highly complex, evaluation set where the results are manually scored. This can provide a high-quality assessment of a method’s capabilities, but it also lacks the robustness of a larger dataset and risks introducing bias into the evaluation, since the ones developing the method are also the ones writing and scoring the results. Agents can also have problems generating a consistent answer over multiple iterations, due to variability in the models, environment, or problem state. This added randomness poses a much larger problem to smaller, complex evaluation sets.
6.3 Impact of Data Contamination and Static Benchmarks
Some researchers evaluate their agent implementations on the typical LLM benchmarks. Emerging research indicates that there is significant data contamination in the model’s training data, supported by the observation that a model’s performance significantly worsens when benchmark questions are modified. This raises doubts on the authenticity of benchmark scores for both the language models and language model powered agents.
Furthermore, researchers have found that “As LLMs progress at a rapid pace, existing datasets usually fail to match the models’ ever-evolving capabilities, because the complexity level of existing benchmarks is usually static and fixed”. To address this, work has been done to create dynamic benchmarks that are resistant to simple memorization. Researchers have also explored the idea of generating an entirely synthetic benchmark based on a user’s specific environment or use case. While these techniques can help with contamination, decreasing the level of human involvement can pose additional risks regarding correctness and the ability to solve problems.
6.4 Benchmark Scope and Transferability
Many language model benchmarks are designed to be solved in a single iteration, with no tool calls, such as MMLU or GSM8K. While these are important for measuring the abilities of base language models, they are not good proxies for agent capabilities because they do not account for agent systems’ ability to reason over multiple steps or access outside information. StrategyQA improves upon this by assessing models’ reasoning abilities over multiple steps, but the answers are limited to Yes/No responses. As the industry continues to pivot towards agent focused use-cases additional measures will be needed to better assess the performance and generalizability of agents to tasks involving tools that extend beyond their training data.
Some agent specific benchmarks like AgentBench evaluate language model-based agents in a variety of different environments such as web browsing, command-line interfaces, and video games. This provides a better indication for how well agents can generalize to new environments, by reasoning, planning, and calling tools to achieve a given task. Benchmarks like AgentBench and SmartPlay introduce objective evaluation metrics designed to evaluate the implementation’s success rate, output similarity to human responses, and overall efficiency. While these objective metrics are important to understanding the overall reliability and accuracy of the implementation, it is also important to consider more nuanced or subjective measures of performance. Metrics such as efficiency of tool use, reliability, and robustness of planning are nearly as important as success rate but are much more difficult to measure. Many of these metrics require evaluation by a human expert, which can be costly and time consuming compared to LLM-as-judge evaluations.
6.5 Real-world Applicability
Many of the existing benchmarks focus on the ability of Agent systems to reason over logic puzzles or video games. While evaluating performance on these types of tasks can help get a sense of the reasoning capabilities of agent systems, it is unclear whether performance on these benchmarks translates to real-world performance. Specifically, real-world data can be noisy and cover a much wider breadth of topics that many common benchmarks lack.
One popular benchmark that uses real-world data is WildBench, which is sourced from the WildChat dataset of 570,000 real conversations with ChatGPT. Because of this, it covers a huge breadth of tasks and prompts. While WildBench covers a wide range of topics, most other real-world benchmarks focus on a specific task. For example, SWE-bench is a benchmark that uses a set of real-world issues raised on GitHub for software engineering tasks in Python. This can be very helpful when evaluating agents designed to write Python code and provides a sense for how well agents can reason about code related problems; however, it is less informative when trying to understand agent capabilities involving other programming languages.
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