Active Evaluation Acquisition for Efficient LLM Benchmarking

We sincerely thank the reviewer for their detailed feedback and strong endorsement of our work. We address each point below:

Terminology: "Prompts" vs "Examples"

We thank the reviewer for highlighting the potential confusion in our use of the term "prompt." We use "prompt" to mean benchmark examples rendered with the dedicated prompt templates, which is the exact input to the target LLM. This distinction is important because some benchmarks/datasets apply different prompt templates to the same examples - for instance, natural_questions in the HELM benchmark is evaluated in two modes with different prompt formats. This is why we focus on prompt-level evaluation efficiency rather than just example selection.

We will revise the paper to clarify this terminology early on to avoid confusion with other types of prompt-related work.

Definition of Evaluation Scores (Y)

We appreciate the request for clarity regarding the definition of Y. In our formulation, Y_m represents the collection of evaluation scores for model m across all prompts in benchmark X. These scores can indeed be of mixed types - binary accuracy, continuous metrics like F1, etc. - depending on the specific dataset within the benchmark. This is explicitly mentioned in Section 2: "These scores may be of mixed types - for instance, some datasets might report binary accuracy while others use continuous metrics like F1 scores."

We will improve clarity by providing a more explicit definition earlier in the paper.

Probability Formulation of Eq.1

We appreciate the reviewer's questions about the formal definitions in our probability formulation. The formulation in Equation (1) is indeed meant to be understood from a Bayesian perspective, and p(Y^(u)_m'|Y^(o)_m',X) represents the conditional likelihood of the unobserved scores Y^(u)_m' given the observed scores Y^(o)_m' and prompts X.

Equation (1) represents an ideal case where we could directly optimize for the subset o* that maximizes the likelihood of correctly predicting unobserved scores. However, as we note in the subsequent paragraph: "the values Y^(u)_m' for a test model m' are unknown before acquisition, making direct optimization impossible." In practice, we cannot directly optimize this objective since Y^(u)_m' is unknown. Instead, we train policies that approximate this optimization based on historical evaluation data.

In our revision, we will add an intuitive explanation of this formulation explaining the intuition behind our probability formulation to avoid confusion.

Method Overhead and Implementation

The reviewer raises an excellent point about discussing implementation overhead. We provide a computational complexity analysis in Appendix G showing that our method adds minimal overhead compared to the LLM inference costs it saves. Specifically:

1. The one-time training of the neural process model and acquisition policy takes 2-3 hours on a standard GPU
2. The policy network is lightweight, consisting of only 2-3 linear layers that execute in milliseconds
3. The 43-92% reduction in required evaluations vastly outweighs this overhead

In our revision, we will highlight this information in the main text and clarify that we will release our implementation to help practitioners adopt our method.

Distinction from Active Testing and Active Learning

We thank the reviewer for the suggestion to clarify our positioning relative to active testing and active learning. We will revise the related work section to more clearly articulate that:

1. Active Learning typically selects examples to label for training a model
2. Active Testing focuses on reducing the labeling cost for evaluating model performance
3. Our Active Evaluation Acquisition (AEA) specifically targets reducing the number of prompt evaluations needed during LLM benchmarking

We use "acquisition" to emphasize the sequential process of obtaining evaluation outcomes, but we agree this could be presented more clearly. We will revise to better own our contribution and clarify the distinctions between these related but different approaches.

Figure Labeling

We appreciate the suggestion about labeling our method in the figures. We will update all figures to clearly label our method as "AEA (ours)" or similar to distinguish it from baseline approaches.

Typo on line 207

Thank you for catching this. We will correct it in the revised version.

We appreciate the reviewer's thorough reading of our work and are grateful for the strong endorsement. We believe the requested clarifications will further strengthen the paper.

We appreciate the reviewer's feedback and concerns. Below we address the specific points raised:

Evidence for Prompt Correlations

The reviewer questions our assumption that evaluation scores across prompts are correlated. This correlation is well-documented in prior literature on LLM evaluation, including in works by Perlitz et al. (2023) and Polo et al. (2024), which we cite. Additionally, we present empirical evidence in Appendix F.4 through a detailed analysis of factors affecting prediction accuracy. This analysis shows that similar prompts (in embedding space) typically yield similar evaluation outcomes, confirming the existence of exploitable dependencies.

Benchmark Selection and Generalizability

Regarding the concern about using only 5 benchmarks, we respectfully note that our selection includes a diverse set of prominent LLM benchmarks that cover different evaluation paradigms:

• AlpacaEval (win rate against reference models)
• HELM-Lite (multiple tasks and metrics)
• OpenLLM Leaderboard (multiple-choice QA)
• MMLU (subject-specific academic tests)
• Chatbot Arena (human preference judgments)

These benchmarks represent the most widely used evaluation frameworks in the field and employ different scoring mechanisms, ensuring our method's applicability across diverse evaluation settings. Together, they comprise over 56,000 evaluation prompts across dozens of tasks and metrics, providing robust evidence for generalizability.

Computational Efficiency of RL-based Approach

The reviewer expresses concerns about the computational cost of our RL-based policy. We address this directly in Appendix G with a computational complexity analysis showing that:

1. The one-time training of our policy and neural process model is computationally inexpensive (typically 2-3 hours on a single GPU)
2. During inference, our policy network adds negligible computational overhead compared to the cost of running LLM inference
3. The savings from reducing the number of required evaluations (65-92% fewer) vastly outweigh any overhead from our method

Our approach makes large-scale LLM evaluation significantly more accessible, especially when evaluating resource-intensive models or when using costly API calls for closed models.

Statistical Significance

Regarding statistical significance, our evaluation compares the convergence behavior of different acquisition policies across multiple runs. The standard deviations reported in our figures and detailed in Tables 1-5 provide evidence of the consistent performance advantages of our approach over baseline methods.

Our results in Figure 1 clearly demonstrate that as the acquisition budget increases, our RL-based method consistently achieves lower error rates compared to baseline methods. For instance, on MMLU (Figure 1d), our method reaches an error of approximately 0.02 with just 50 prompts, while random sampling requires nearly 100 prompts to achieve the same accuracy. This pattern is consistent across all benchmarks, with our method's error decreasing more rapidly as more prompts are acquired. This faster convergence to low error rates demonstrates our method's superior efficiency in identifying the most informative prompts for evaluation.

Visual Representation

We acknowledge the reviewer's suggestion about including more visual representations. Figure 1 in our paper illustrates various benchmarks with performance plots showing acquisition budget versus absolute error. In the revised version, we will add a schematic diagram illustrating our overall approach to make the workflow more accessible to readers.

We appreciate the opportunity to address these concerns and believe our work makes a significant contribution to efficient LLM evaluation, with strong empirical evidence supporting our claims.

We sincerely thank the reviewer for their thorough evaluation of our work and the positive recommendation. Below, we address the specific concerns and questions raised:

Evaluation Metrics and Result Interpretation

The reviewer raised questions about our use of absolute error as an evaluation metric and the distinguishability of results. We chose absolute error because it directly measures how accurately our method estimates the true benchmark performance, which is the primary goal of our work.

Regarding the distinguishability of results: This is due to the fact that given enough acquisition budget, all selection methods can eventually approximate the overall performance. However, as clearly demonstrated in our figures, our RL-based approach decreases the error much more quickly as the acquisition budget increases, which demonstrates its superiority. In Appendix F.5, we conduct a detailed analysis comparing our approach to random sampling, showing that we can achieve the same level of error using 35-92% fewer prompts across different benchmarks. This substantial reduction in required evaluations represents significant cost savings in practical applications.

For instance, on MMLU, our method achieves the same accuracy with just 35 prompts as random sampling does with 100 prompts - a 65% reduction that would translate to proportional cost savings when evaluating new models.

Statistical Significance and Random Seeds

We conducted experiments with three random seeds to keep comparison consistent across all methods. The larger variance observed in baseline methods actually further demonstrates the superiority of our approach, which maintains more consistent performance across different initialization seeds. For better result representation, we have included detailed tables in the appendix (Table F.1) that show performance statistics across all runs.

Neural Process Background

We appreciate the suggestion to provide more background on neural processes. In the revised version, we will expand Section 3.1 to include:

1. A more intuitive explanation of how neural processes model stochastic functions
2. A brief comparison with related approaches like Gaussian Processes
3. A clearer explanation of why neural processes are particularly well-suited for capturing dependencies across evaluation prompts

This additional background will make our methodology more accessible to readers who may be less familiar with these techniques.

We appreciate the reviewer's positive assessment of our paper's organization, algorithms, and figures. We're committed to further improving the clarity and impact of our contribution in the final version.

We sincerely thank the reviewer for their thorough evaluation of our work and the positive recommendation. Below, we address the points raised:

Practical Applications of Efficient Evaluation

We appreciate the suggestion to highlight practical applications of efficient evaluation in the introduction. In the revised version, we will emphasize how our method can be particularly valuable during model development for:

1. Enabling more frequent intermediate evaluations during training, allowing earlier detection of issues
2. Supporting more extensive hyperparameter tuning by reducing evaluation costs per configuration
3. Facilitating rapid comparisons between model variants during research iterations
4. Reducing API costs for closed-source models during the development phase
5. Decreasing environmental impact through reduced compute requirements

Implementation Workflow for New Models

Regarding how to evaluate a new model from scratch, we thank the reviewer for prompting us to better highlight our existing detailed workflow in Appendix A, which provides a comprehensive description of the training and deployment procedure. As described there, our approach involves:

1. One-time setup: Training the neural process model and acquisition policy on historical benchmark data
2. Per-model evaluation: Sequentially selecting prompts, obtaining scores, and predicting remaining scores

Once this setup is complete, the evaluation process for each new model adds minimal computational overhead compared to the actual LLM inference costs.

Complexity vs. Random Sampling Trade-off

We acknowledge the valid concern about complexity versus simplicity. While random sampling is indeed simpler, our results demonstrate substantial efficiency improvements (43-92% fewer evaluations) that justify the additional setup complexity in many scenarios. As detailed in Appendix F.5, our approach offers particularly compelling benefits for:

• Organizations conducting ongoing LLM development and benchmarking
• Evaluations of large or expensive models where each prompt evaluation is costly
• Scenarios requiring adaptation to new model families or previously unseen prompts

The choice between approaches depends on the specific evaluation context, with our method becoming increasingly beneficial as evaluation costs or frequency increase. We'll ensure these considerations are more prominently highlighted in the main text of our revision.

We sincerely thank the reviewer for their thoughtful assessment of our work. Below, we address the key points raised:

Concern about Subset Selection and Fairness Across Models

The reviewer raises a valid concern about whether variations in selected subsets for different groups of LLMs might affect fair comparison between models. This is indeed an important consideration that we would like to clarify:

1. Our framework maintains fair comparability by using both the acquired scores on selected prompts AND predicted scores on the remaining prompts for final benchmark estimation. This ensures that all models are evaluated on the full benchmark (either via direct evaluation or accurate prediction), enabling fair comparison regardless of which specific prompts were selected for each model.

2. As demonstrated in Table 2 of our paper, our prediction-based estimation approach ("w/ pred") consistently outperforms direct aggregation of only the acquired evaluation scores ("w/o pred"). This confirms that our method maintains benchmark integrity while improving efficiency.

3. We conducted a specific experiment examining model bias (Section 5, Fig. 2) where we deliberately evaluated on models from different families than those used for training. The results demonstrate that our method remains effective even in this challenging scenario, indicating robustness to the training dataset composition.

Adaptation to New Models and Benchmarks

The reviewer asks about our method's ability to adapt to new models and benchmarks, which is indeed crucial for LLM evaluation:

1. For new models: Our approach is explicitly designed for evaluating new, unseen models. The train-test splits in our experiments (particularly for Open LLM Leaderboard where we use chronological splitting) ensure that our method is validated on truly new models not seen during training. The consistently strong results across various benchmarks demonstrate the generalization capability to new models.

2. For new prompts (cold start problem): We explicitly address this in Section 5 with a dedicated experiment (Fig. 3) where 15 MMLU subsets are treated as "cold start prompts" with no historical scores. Our RL-based policy successfully generalizes to these completely new prompts due to our policy architecture that explicitly incorporates prompt representations.

3. For adapting to distribution shifts: We could further enhance adaptation through continual learning approaches where the neural process model and acquisition policies are jointly updated as new models are evaluated - a promising direction for future work that we mention in the conclusion.

Missing Reference on Active Evaluation

We thank the reviewer for highlighting the paper "Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons". This is indeed a relevant work that should be discussed in our paper. This paper focuses on efficiently identifying top-ranked NLG systems using pairwise comparisons, specifically applying dueling bandit algorithms to reduce the number of human annotations required.

Our work shares the goal of improving evaluation efficiency but addresses a complementary problem: reducing the number of prompts needed to benchmark individual LLM performance rather than ranking systems through pairwise comparisons. We will incorporate a thorough discussion of this paper in our revised manuscript, acknowledging their valuable contributions to efficient evaluation methods and highlighting how both approaches serve the broader goal of making NLG/LLM evaluation more practical and accessible.

Additional Comments

We note the reviewer's positive assessment of our paper's strengths, including:

• The importance of the research problem
• The soundness of our proposed methods
• The clarity and organization of the paper

These align with our goal of addressing a practical challenge in LLM development while maintaining scientific rigor.

Conclusion

We appreciate the reviewer's recommendation and constructive feedback. We believe our work makes a significant contribution by dramatically reducing evaluation costs (by 43-92% across benchmarks) while maintaining accurate performance estimation. This enables more efficient and sustainable LLM development, especially for researchers with limited computational resources.

In the revised version, we will:

1. Further clarify how our approach maintains fair comparability between models
2. Incorporate the suggested reference and discuss its relevance
3. Expand our discussion on adaptation to new models and benchmarks

Thank you for the opportunity to address these points and strengthen our paper.