Active Evaluation Acquisition for Efficient LLM Benchmarking
Authors: Yang Li
Jie Ma
Miguel Ballesteros
Yassine Benajiba
Graham Horwood
Venue: ICML 2025 poster (2025)
Keywords: LLM Evaluation
Subset Selection
Active Learning
Primary area: applications->language_speech_and_dialog

Abstract: As large language models (LLMs) become increasingly versatile, numerous large scale benchmarks have been developed to thoroughly assess their capabilities. These benchmarks typically consist of diverse datasets and prompts to evaluate different aspects of LLM performance. However, comprehensive evaluations on hundreds or thousands of prompts incur tremendous costs in terms of computation, money, and time. In this work, we investigate strategies to improve evaluation efficiency by selecting a subset of examples from each benchmark using a learned policy. Our approach models the dependencies across test examples, allowing accurate prediction of the evaluation outcomes for the remaining examples based on the outcomes of the selected ones. Consequently, we only need to acquire the actual evaluation outcomes for the selected subset. We rigorously explore various subset selection policies and introduce a novel RL-based policy that leverages the captured dependencies. Empirical results demonstrate that our approach significantly reduces the number of evaluation prompts required while maintaining accurate performance estimates compared to previous methods.

Active Evaluation Acquisition for Efficient LLM Benchmarking
Yang Li
Jie Ma
Miguel Ballesteros'
Yassine Benajiba' Graham Horwood
Abstract
In this work, we aim to improve evaluation efficiency by reducing the number of evaluation prompts needed. We observe that evaluation prompts are often correlated, where model's 's performance on certain prompts tends to corre- spond with its performance on related ones. To leverage this, we build a model to capture dependencies across prompts and predict evaluation scores based on observations from a subset of prompts. Our goal becomes identifying the mini- mal subset that can accurately recover the evaluation scores for the remaining prompts.
As large language models (LLMs) become in- creasingly versatile, numerous large scale bench- marks have been developed to thoroughly assess their capabilities. These benchmarks typically consist of diverse datasets and prompts to evalu- ate different aspects of LLM performance. How- ever, comprehensive evaluations on hundreds or thousands of prompts incur tremendous costs in terms of computation, money, and time. this work, we investigate strategies to improve eval- uation efficiency by selecting subset of exam- ples from each benchmark using a learned policy. Our approach models the dependencies across test examples, allowing accurate prediction of the evaluation outcomes for the remaining examples based on the outcomes of the selected ones. Con- sequently, we only need to acquire the actual eval- uation outcomes for the selected subset. We rig- orously explore various subset selection policies and introduce a novel RL-based policy that lever- ages the captured dependencies. Empirical results demonstrate that our approach significantly re- duces the number of evaluation prompts required while maintaining accurate performance estimates compared previous methods.
Instead of using a fixed subset across all models, we pro- pose selecting unique subsets for each model through ac- tive evaluation acquisition (AEA). This approach recog- nizes that models may have varying strengths for example, one model may excel in arithmetic reasoning while another shows stronger commonsense reasoning. By tailoring the subset of prompts for each model, we ensure a more accu- rate and targeted evaluation of its capabilities. Furthermore, our dynamic acquisition process adapts in real time as eval- uation scores are gathered. As the model's performance on initial prompts is observed, the system adjusts subsequent prompt selections to better explore areas of uncertainty or confirm early findings. This iterative approach not only enhances the accuracy of performance estimation but also reduces redundancy by avoiding prompts that are likely to yield predictable results, thereby saving computational re- sources and time. Importantly, the final evaluation score is derived from both the acquired scores on selected prompts and predicted scores on the remaining prompts, ensuring comparability across models is maintained.
1. Introduction
As large language models (LLMs) become increasingly ver- satile, comprehensive benchmarks have emerged to assess their capabilities. However, these evaluations incur substan- tial costs for example, evaluating on the HELM benchmark requires 4,200 GPU hours for 176B BLOOM model and $9.337 for text-davinci-O0t API calls (Liang et al., 2022). Such costs hamper development by preventing frequent eval- uation during model training and extensive hyperparameter tuning during inference.
Our contributions include: 1) We tackle LLM evaluation ef- ficiency through dependency modeling and subset selection, connecting it with subset selection literature. 2) We design a generative model that captures dependencies across prompts and handles mixed-type evaluation scores. 3) We thoroughly evaluate existing subset selection algorithms on several pop- ular LLM benchmarks. 4) We develop new subset selection policies, with our RL-based approach achieving the best performance using minimal acquisition budget.
Amazon Web Service, Correspondence to: Yang <yl- izam amazon.com>.
2. Problem Formulation
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).
Consider a benchmark X with N prompts, X = (ax When evaluating a model m on this benchmark, we obtain

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Active Evaluation Acquisition for Efficientt LLM Benchmarking
evaluation scores Ym (UmnJn=11- A leaderboard for this benchmark typically contains evaluation scores for M models, denoted as JmmM= These scores may be of mixed types- for instance, some datasets might report binary accuracy while others use continuous metrics like Fl scores.
Specifically, the function fm is implicitly parameterized by latent vector Zm, and the generative model then follows
p(Y..") Y .X = p(zm Ydl), X)p(Y(h)) | Voli X)dam
(2)
Since the integration is over high dimensional latent space, we optimize the evidence lower bound (ELBO) following variational autoencoder (VAE) (Kingma & Welling, 2013)
For a new model m' to be evaluated, our goal is to ac- quire evaluation scores for only a subset of prompts y-lo) = (ymoo o S [I,...,NN} }N} while predicting the scores y-() (ym'u == , NN \ o} for the remaining .NI...,,.........
log yol)), x)
prompts. The key challenge is capturing the dependencies across prompts to ensure accurate prediction of the unob- served scores. We model these dependencies through the conditional distribution p(Y y X). Since the set of prompts to acquire their scores is not predefined, we must m² y
p(Y 2ms vdo) Xx ())(z Zm Y .X)
"(sm yh x) log
((3m Yn yrfo) .))
where g(2m
Yr(o) X) and p(zm Ym X) repre-
sent the posterior and prior distributions, respectively.
estimate p(Y (1() m²
X) for all possible subsets u and o.
To ensure model represents valid stochastic process, we adhere to the conditions stated by the Kolmogorov Fx- tension Theorem (Oksendal, 2013): (finite) exchangeability and consistency. The exchangeability condition requires that the joint distribution p(Ym) remain unchanged under permutations of its elements, which we satisfy by using per- mutation invariant networks to parameterize both the prior and posterior distributions. The consistency demands that marginalizing out part of Yn, yields the same distribution as that defined on the original prompt 2T,. This condition is met when the approximate posterior equals the true poste- rior, which we achieve by training the model with sufficient data from diverse model evaluations so that the lower bound approaches the actual likelihood. In order to handle textual prompts, we utilize a pretrained embedding model rep- resent each prompt IR vector. Please see Sec. B for implementation details.
Given a budget K for acquiring evaluation scores and the above generative model, our objective is to find an optimal subset o" C NY.. where [o*| = K, such that:
argmax p(Y ml ( X),
OEP([N],K)
where P[[N), K) represents all subsets of (1...., N with cardinality K. Note that this optimal subset could be differ- ent for each model being evaluated.
This objective presents two main challenges. First, the val- ues yx(1) for a test model m' are unknown before acquisition, making direct optimization impossible. Second, the number of possible subsets grows combinatorially with N, making exhaustive search infeasible. Our solution addresses these challenges through an active learning inspired approach that iteratively selects prompts based on observed scores.
3.2. Evaluation Acquisition Policy
3. Method
Given the generative model across subsets of evaluation prompts, we now develop acquisition policies to select an optimal subset of prompts for acquiring their true evaluation scores, while the remaining scores will be predicted by the conditional p( p(Y (( X).
3.1. Modeling Dependencies via Neural Processes
In this section, we aim to capture the dependencies across evaluation prompts by modeling the conditional distribution p( (Yy!")) y!o) X). We represent the relationship between the prompts and their evaluation scores a stochastic pro- cess F : X' + V, where X' and y denote the spaces of prompts and their corresponding evaluation scores, respec- tively. Evaluating on benchmark X can be interpreted as observing fnite-dimensional marginal distributions of this stochastic process. Specifically, evaluation scores Ym repre- sent function values (z,)) A for a particular function, fm, sampled from the distribution of functions.
3.2.1. RANDOM POLICY
A random acquisition policy selects a subset of size K at random to acquire the evaluation scores. Here, we consider two variants: Uniform Sampling and Stratified Sampling (Perlitz et al., 2023). Uniform Sampling selects K prompts uniformly from X, while Stratified Sampling considers the size of different datasets and ensures each dataset equally represented. The stratified sampling has been verified effec- tive on HELM benchmark (Perlitz et al., 2023).
Neural Processes (NPs) (Garnelo et al., 201 10;;;;; Kim et al., 2019) provide a flexible and scalable approach to modeling such stochastic processes. They combine the strengths of neural networks and Gaussian Processes to predict outputs for new inputs by conditioning on a set of context points.
3.2.2. STATIC POLICY
A static acquisition policy determines the set of prompts to be evaluated beforehand, and each model to be evaluated
2

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Active Evaluation Acquisition for Efficient LLM Benchmarking
acquires the evaluation scores on the same set of prompts. We assess the following two types of static policies:
Algorithm 1 Active Evaluation Acquisition
Require: Budget K, model m to be evaluated, Neural Process p
1: 0, Y 0, u NJ
Clustering Given the embedding for each prompt, we group them into K cluster, then select one prompt in each cluster that is closest to the cluster centroid. We denote this approach as Clustering- pretrained sentence embedding. we can use the learned embedding from an Item Response Theory (IRT) model (Hambleton & Swaminathan, 2013; Embretson & Reise, 2013), which represents the difficulty and discriminability of each prompt. The Clustering- IRT method, proposed in (Polo et al., 2024), has been successfully applied on several public LLM benchmarks. Inspired by (Vivek et al., 2023), which selects representative examples by clustering based groups prompts
2: while |a| < do
3: Select prompt i according to (5), (6), or (9)
4: Evaluate model m on prompt to get evaluation score y
Instead of using the
5: o oo Y. Y! u (Y J. u = u \ {i}
6: end while
7: Predict the evaluation scores for the remaining prompts y((1) p((mm v X)
Dynamic acquisition sequentially acquires evaluation scores and simultaneously refines the uncertainty of predictions, enabling real-time adaptation based on observed perfor- mance. AEA reduces redundancy by avoiding predictable evaluations and focusing resources on the most informative prompts. Please refer to Algorithm for pseudo-code of the active acquisition process.
on prediction confidence, Clustering-Seot based their evaluation scores on the training set. Each prompt n is represented by a vector of evaluation scores, with the size of the vector corresponding to the number of evaluated models in the training set. The Clustering- method has been used as a baseline in (Polo et al., 2024).
Uncertainty Sampling Inspired by uncertainty sampling method widely used in active learning literature (Ren et al., 2021; Yang al., 2015; Raj & Bach, 2022), where the most uncertainty data point under the current predictor is chosen to query its label, we select the next prompt to be evaluated based on the uncertainty of p( pY:!!" yI(o), X). Here, o contains the evaluated prompts so far, and E is one of the candidate prompts to be selected. We choose the prompt with the highest entropy:
Combinatorial Optimization Given the model p p(("") y .X), static acquisition policy can be derived by searching over the training set to find the optimal subset of prompts that gives the most accurate prediction of the remaining prompts. This is a typical combinatorial opti- mization problem, which is NP-Hard. Here, we employ a sequential approach that selects one prompt at a time until K prompts are selected. Starting from an empty set o = 0, the next prompt i := {I,.... [I, \ o chosen to minimize the prediction error over the training set, i.e.,
N
i = arg max H(Y (i)
y .).
(5)
In practice, we estimate the entropy by sampling multiple times and computing the sample variance.
i arg min Eym-pp Eocats (7((')) y-(') 12,
(4)
N'u
Information Gain Given the latent variable based neural
where of = fi') and u' = u. { denote the updated ob- served and unobserved set after acquiring evaluation scores for prompt i'
process model (2), where the latent variable parameterizes the stochastic process, a forward acquisition policy is to select the prompt that provides the most information about the latent variable zm We use the conditional mutual information to measure the amount of information:
{=(x)) m indicates predicted scores sampled from
the neural process model p(Y:!=') y=(oo, x). The expec- tation is estimated by Monte Carlo sampling. For notation simplicity, the above equation computes the mean squared error on prompts u': however, in practice, different datasets may use different metrics. Additionally, these differences may be weighted depending on the dataset size. Please refer to Algorithm 3 for pseudo-code of the selection process. Note that this approach has complexity of O(KMN), which could be prohibitive when the benchmark is large.
= arg max [(Y((() 2m Y(o) .X)
ieu
arg max H H (Y - Epgg H(m | (800 y,o),
sea
arg min Bpg (p())) (o .x) (zm. P(0 x(o),
(6)
The third equation follows because the observed set o is the same for any candidate i E u. The expectation is estimated by Monte Carlo sampling. Note that the entropy is estimated based on predicted y-()) rather than the true evaluation score as the true score is unknown before acquisition. At each acquisition step, the entropy must be estimated for each candidate prompt i E u. Therefore, the total complexity is (KN), which could be prohibitive for large benchmarks.
3.2.3. DYNAMIC POLICY
Instead of acquiring the same set of evaluation scores for each model, we propose dynamically acquiring adaptive sub- sets for different models, a method we term Active Evalua- tion Acquisition (AEA). This approach tailors the selection of prompts to each model's 's specific strengths and weak- nesses, providing a more accurate and efficient evaluation.
3

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Active Evaluation Acquisition for Efficiintt LLM Benchmarking
Reinforcement Learning The active acquisition process can be formulated as Markov decision process (MDP), where the state consists of the currently evaluated prompts and their scores, and the action space contains the remaining prompts to be evaluated. To solve the MDP, a reinforcement learning agent sequentially acquires new evaluation scores based on the current state. After acquiring evaluation score for prompt i, the current state transitions to a new state as follows: - u (i), Y & Yo U (Yx1)), }. When the agent acquires evaluation scores for K prompts, the acqui- sition process terminates, and the agent receives a reward based on the prediction accuracy for the remaining prompts. In basic setup, the agent would only receive reward at the end of the acquisition process (after selecting K prompts) based on the prediction accuracy for the remaining prompts. However, this delayed reward structure poses a typical tem- poral credit assignment problem, which complicates the learning of an effective agent, especially when the trajectory is long (Minsky, 1961; Sutton, 1988). To address this issue, we design an intermediate reward function that provides immediate feedback after each acquisition action i. Specifi- cally, the reward measures the improvement in prediction accuracy per unobserved prompt:
In addition to providing intermediate rewards, we propose using the neural process to assist the agent with auxiliary information. Specifically, the neural process can predict the evaluation scores for unobserved prompts based on the observed scores in the current state. By sampling multiple times, the neural process can inform the agent about the un- certainties of these unobserved scores. The predicted scores and their uncertainties on the unobserved prompts allow the agent to anticipate future states and guide its exploration. For instance, if the neural process is very confident about the score of currently unobserved prompt, then acquiring its real score would be redundant. The auxiliary informa- tion helps the agent make more informed decisions about which prompts to evaluate next, improving the efficiency and accuracy of the active acquisition process.
3.3. Cold Start Problem
As language models advance with emergent capabilities, benchmarks must expand with new prompts for which no evaluation scores are initially available. This cold start prob- lem introduces two key challenges: generalizing the neural process to predict scores on new prompts, and determining which new prompts to select.
i( = xu(
E By
[1/(6=) -
To address neural process generalization, we employ a semi-supervised approach where new prompts are treated as unlabeled data. Specifically, we incorporate predicted scores with high confidence into the training process through pscudo-labeling (Lee et al., 2013; Xie et al., 2020; Du et al., 2020). In our preliminary experiments, we also tested sev- eral common semi-supervised learning approaches, includ- ing entropy minimization (Grandvalet & Bengio, 2004) and consistency regularization (Tarvainen & Valpola, 2017), but found pseudo-labeling consistently performed best. A sys- tematic exploration of semi-supervised neural process train- ing remains as future work.
|u]
lu'l
(7)
where o' = {i) and w/ = u {i) denote the observed and unobserved sets after acquiring prompt i. The expectations are taken over predicted evaluation scores sampled from the neural process model, with Y,(") ~ -((Y Y .X) before acquiring prompt i and ((d)) ~ p(Yh'" Yy,,), X)
after the acquisition. Note that this intermediate reward follows the potential function structure (Ng et al., 1999), therefore, it will not change the optimal policy.
Given this reward structure, the goal of the agent is to learn a policy nt that maximizes the expected cumulative reward over the trajectory of K acquisitions:
While static policies are limited by their reliance on his- torical scores, our dynamic approach enables continuous adaptation through sequential active acquisition. The key to handling previously unseen prompts lies in our novel policy architecture that explicitly incorporates prompt representa- tions. At each acquisition step, the policy network $hS takes two inputs: the acquired scores Y((o) and the embeddings of candidate prompts to be evaluated, where these embeddings are shared with the neural process model. The network out- puts a vector in the same space as the prompt embeddings, and the selection probability is determined through inner products:
K Σ r(or, it)
π²" = arg max E, BTON
(8)
where (01, 41, i... 0K, ik) represents trajectory of states and actions under policy æ, and r(or, it) is the reward received after selecting prompt i, given the current observed set Og. Note that the reward computation is only required during training when we have access to all the evaluation scores. During testing, the next prompt to be evaluated will be directly selected by the policy:
eachu(yto) in ((o),aa
i = arg max Ti Y (o)
X).
(9)
Ti V(O) x)= = EiEn ea:-h(y'l (to)n) (a:)ee))
(10)
Since the policy network has constant computational cost, the total complexity of the acquisition process remains (K) regardless of the benchmark size.
where (atheu hee denote the embeddings of candidate prompts. This dot-product architecture enables the policy to handle
4

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Active Evaluation Acquisition for Efffcciett LLM Benchmarking
arbitrary action spaces while maintaining awareness of avail- able actions (Jain et al., 2020). Please refer to Appendix E for implementation details.
0.925
UniformSampling
StratifiedRendom
0.022
Clustering-Embee
Clustering- Score
Clustering-IRt
4. Related Works
0003
combinatorrallppimiioooon
0.935
Uncertainty
Active Learning Active learning (Fu al., 2013; Konyushkova et al., 2017; Yoo & Kweon, 2019) addresses the problem of having learner select specific examples to query an oracle for their labels, with the goal of learning better model using as few labeled examples as possible. In contrast, our proposed AEA framework focuses on evaluat- ing a model with fewer examples to accurately predict the evaluation scores for the remaining examples.
Latentinfociain
RL
Prompts
(a) AlpacaEval
0.10
6.14
0.32
Ener
0:32
0.30
0.00
0.28
0.43
0.06
5.00
0.04
2.44
Active Testing Active testing (Kossen et al., 2021) re- duces the labeling cost by selectively choosing test points to label, ensuring sample-efficient model evaluation. This approach has been adapted LLM evaluation (Huang et al., 2024) and preference data selection (Ashury-Tahan et al.,
0.02
0.58
25
50
75.
100
335
150
375
200
25
50
300
125
350
175
200
&Prmppss
+Prmpps
(b) HELM-Lite
(c) OpenLLM Leaderboard
6:30
0.002
0.222
8.08
2024). While this aligns with the overarching goal of effi- cient evaluation, our work specifically targets reducing the cost of running evaluations rather than minimizing labeling costs.
Abs. Firor
0:022
2.05
0:33
0.082
large number of prompts,
2.4
0.75
0.032
602
0.23
Computerized Adaptive Testing IRT provides a prin- cipled framework for adaptive testing (Liu et al., 2024), selecting test items based on their difficulty and discrim- ination parameters. Zhuang et al. (2023) further applied this technique to LLM evaluation. While IRT methods offer interpretable parameters and theoretical guarantees, they typ- ically assume item independence. Our approach differs by explicitly modeling dependencies between prompts through a neural process and learning adaptive selection strategies via RL, rather than relying on static item parameters.
Prompts
Prompts
(d) MMLU
(c) Chatbot Arena
Figure I. Experiment results on five LLM benchmarks, with shaded areas indicating the standard deviation over three runs.
comprehensively assess these methods and further propose actively selecting evaluation examples.
5. Experiments
Efficient LLM Benchmarking As LLMs continue de- velop and scale, ongoing efforts aim to create benchmarks that comprehensively assess their capabilities. A notable trend in these benchmarks is their evolution from single-task assessments (Bowman et al., 2015; Rajpurkar et al., 2016) to multi-task benchmarks (Wang et al., 2018; 2019), and ultimately to massively multi-task evaluations (Srivastava et al., 2022; Liang al., 2022; Hendrycks al., 2020). The ever-increasing evaluation cost has encouraged researchers to develop efficient evaluation approaches. BIG-bench Lite (Srivastava et al., 2022) and BIG-bench Hard (Suzgun et al., 2022) evaluate on subset of BIG-bench tasks, and Ye et al. (2023) propose clustering BIG-bench tasks and selecting the examples that are closest to cluster centers. Perlitz et al. (2023) found that the model rankings on HELM can be accurately obtained by evaluating only a fraction of the examples. Vivek al. (2023) propose clustering the evalua- tion examples based on the uncertainty of model predictions, while Polo et al. (2024) suggest clustering examples based on learned features from an IRT model. In this work, we
In this section, we assess various evaluation acquisition policies on several popular LLM benchmarks. We divide the available leaderboard scores into training and test splits. The training split is used to fit the neural processes model, capturing the dependencies across prompts. The acquisi- tion policies are executed for each model in the test split to acquire the evaluation scores for subset of prompts. The evaluation scores on the remaining prompts are predicted based the corresponding neural process model. The final score for each benchmark is computed as a weighted aver- age across datasets, and we report the absolute differences between the predicted scores and the actual scores. Please see Appendix for details.
We conduct experiments on five popular LLM benchmarks: HuggingFace Open LLM Leaderboard (Beeching et al., 2023), MMLU (Hendrycks et al., 2020), HELM--ite (Liang et al., 2022), AlpacaEval 2.0 (Li et al., 2023), and Chatbot Arena (Zheng et al., 2024). For each benchmark, we divide its leaderboard into training and testing splits based on mod- els rather than prompts. When evaluation dates are available
5

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Active Evaluation Acquisition for Efficient LLM Benchmarking
(e.g., for Open LLM Leaderboard), we use chronological splitting ensure testing is performed newer models, better simulating real-world scenarios where we evaluate newly released LLMs. For benchmarks without temporal information, we use random splitting. Detailed descriptions of these benchmarks can be found in Appendix F.
Table I. Comparison of our RL--based acquisition policy with Tiny- Benchmarks (TB) (Polo et al.. 2024), using selected prompts to predict evaluation scores with either the IRT model from TB or our NP model. The metric is the absolute prediction error.
IRT++
NP
AlpacaEval (K=100)
TB
0.027 + 0.002
0.003 1 0.001 0.000
Results Figure 1 presents the main experimental results on 5 LLM benchmarks. We conduct experiments with 3 random seeds for each benchmark and plot the average per- formance and standard deviation throughout the acquisition process. Prompt embeddings are obtained using the SFR embedding model (Meng et al., 2024). For the static cluster- ing based policies, since the selected prompts do not have an inherent order, the acquisition process shuffles the se- lected prompts at random. For the AlpacaEval and Chatbot Arena benchmarks, stratified random sampling is equivalent to uniform sampling since there are only one dataset in each benchmark. Combinatorial optimization is too expensive to run for HELM-Lite, HuggingFace Open LLM Leaderboard, and MMLU due to the large number of prompts. We found that uncertainty and information gain based policies con- sistently fail to explore the action space, leading to worse overall performance. To avoid cluttering the plots, results for uncertainty sampling and information gain based poli- cies are moved to the appendix. Please refer to Appendix F for more analysis.
RL
0.014 + 0.005
0.001
MMLU
TB
0.022 T 0.000
0.016 1 0.000
(K=100)
RL
0.028 1 0.002
0.013 0.000
Open LLM
TB
0.023 + 0.002
0.022 T1: 0.004
(K=200)
RL
0.019 + 0.001
0.018 0.001
0.05
Undormoomppl ng
0.36
6.04
Quateiing Score
Gustering trrbed
0.34
Oastarig kmted
Abo. Error
0.17
2.03
8 0.20
2 0.08
0.26
GUL
25
(0)
100
25
50
100 125
150
L/ 200
Prompps
Prompts
(a) AlpacaEval
(b) HELM-Lite
Figure 2. Evaluate the situation with model bias, where test models are from different model families compared to the training models.
results and analysis.
For all benchmarks, our proposed RL-based acquisition policy achieves the best performance with the lowest acqui- sition budget, demonstrating its superior ability to select informative prompts and accurately estimate benchmark performance. Notably, our method achieves dramatic reduc- tions in required evaluations - requiring only 35 prompts to match the accuracy of random sampling with 100 prompts on MMLU, and similar efficiency gains across other bench- marks (75% reduction on HELM-Lite, 92% on AlpacaEval, see Sec. F.5 for detailed analysis). The stratified random sampling policy performs similarly to uniform sampling. Interestingly, the Clustering-Embed policy does not outper- form the random selection, indicating that the similarity in prompt embedding does not always translate to the similar- ity in evaluation scores. Among the three clustering bause based policies, none consistently outperforms the others. On Al- pacaEval, HELM-Lite, and MMLU, the policies that utilize the evaluation scores (i.e., Claustering-S and Clustering- IRT) perform better, while on the Open LLM Leaderboard and Chatbot Arena, Maassterin-EEbeed perform better. The combinatorial optimization based policy does not perform well, even on the two small benchmarks where it is computa- tionally feasible. We attribute this to a potential distribution shift between the models used for training and those used for testing, suggesting that the static policy optimized on training models does not generalize well to new models during testing. Please see Sec. F3 and F.4 for additional
Additionally, we compare our methods with tinybenchmarks (Polo et al., 2024). Given the selected subsets from tiny- benchmarks, we predict the final benchmark performance using both the IRT++ models provided by tinybenchmark I and our neural process models. Conversely, we also evaluate our prompt selections using both models. Table 1 compares the prompt selections from our proposed RL policy with those from tinybenchmark. The original tinybenchmark se- lect 600 prompts for Huggingface Open LLM Leaderboard, but in our comparison, we select 200 prompts to ensure a fair comparison with our RL policy. The results show that for both prompt selections, using NP produces better bench- mark performance estimates, indicating that our NP model better captures the dependencies and predicts the missing evaluation scores. Given a fixed prediction model (either IRT++ or NP), our RL-based acquisition policy achieves lower error compared to the prompt selections from tiny- benchmark, demonstrating that our RL-based policy is more effective at selecting the informative prompts.
Model Bias An important aspect of efficient benchmark- ing strategies is robustness to model bias. To accurately evaluate future models, which may differ significantly from previously seen models, the strategy must accurately mea- sure model capabilities based on the selected prompts. Our train-test splits based on date for MMLU and Open LLM
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Table Comparison of the final benchmark performance estimation methods. w/ pred indicate the proposed method where the neural process is used to predict the missing evaluation scores. w/o pred indicates the baseline where final performance is an aggregation of the acquired evaluation scores.
AlpacaEval (K=100)
HELM-Lite (K=200)
Open LLM
MMLU (K=100)
Chathot Arena
(K=200)
(K=40)
w/ pred
0.005 = 0.000
0.038 + 0.005
0.022 + 0.002
0.018 + 0.001
0.052 + 0.010 0.036 = 0.009
Uniform
w/o pred
0.012 = 0.001
0.079 = 0.008
0.043 + 0.003
0.042 + 0.003
w/ pred
0.035 = 0.012
0.030 + 0.003
0.017 + 0.002
S-Rand
w/o pred
0.072 = 0.012
0.023 + 0.001
0.038 + 0.001
w/ pred
0.006 = 0.001
0.051 + 0.010
0.021 +: 0.002
0.020 + 0.001
0.052 + 0.014 0.032 = 0.003
C-Embed
w/o pred
0.023 = 0.008
0.116 = 0.003
0.029 + 0.000
0.029 + 0.000
w/ pred
0.004 = 0.001
0.054 + 0.010
0.031 + 0.003
0.014 + 0.002
0.054 + 0.004
C-Score
w/o pred
0.141 = 0.011
0.051 + 0.017
0.086 + 0.002
0.048 + 0.002
0.037 + 0.011
w/ pred
0.003 = 0.001
0.044 + 0.013
0.026 + 0.001
0.015 0.001
0.057 + 0.002
C-IRT
w/o pred
0.069 = 0.003
0.060 + 0.014
0.037 + 0.003
0.041 + 0.006
0.042 + 0.003
w/ pred
0.001 = 0.000
0.030 = 0.005
0.018 + 0.001
0.013 + 0.000
0.034 = 0.006
RL
w/o pred
0.064 = 0.006
0.081 + 0.019
0.063 + 0.018
0.030 + 0.006
0.045 + 0.007
Leaderboard potentially evaluate this situation since model performance tends to improve over time. To further evaluate the performance in the presence of model bias, we divide the models the AlpacaEval and HELM-Lite leaderboards based on their organizations. For HELM-Line Lite, we use pro- prietary models, such as GPT-4 (Achiam et al., 2023) and Claude (Anthropic, 2024), for training and test on open- source models, such as LLaMA (Touvron et al., 2023) and Mistral (Jiang et al., 2023). For AlpacaEval, we do the oppo- site, using open-source models for training and proprictary models for testing.
Although the evaluation scores are missing, we assume the prompts themselves are given, allowing random policies and Clustering- static policy to be evaluated with- out any modifications. However, the Clustering Score and Clustering-IRT policies will never acquire evaluation scores for these 15 subsets since these policies require access to the evaluation scores to determine whether a prompt will be acquired or not. On the other hand, dynamic acquisition policies can easily adapt to the cold start setting, as they acquire evaluation scores sequentially and actively.
Figure 3 presents the results on the synthetic cold start MMLU benchmark. The performance is evaluated over all 57 subsets dur- ing testing. As expected, the Clustering-Score and Clustering- IRT policies do not perform well in the cold start setting because the eval- uation scores on the 15 left- out subsets are never ac- quired. The Clustering- Embed policy performs better than the other clustering based policies it can select the cold start prompts by clustering based on their embeddings. The RL-based acquisition policy again achieves the best perfor- mance estimation. However, it is worth noting that the final estimated benchmark performance is not as accurate as in the fully observed setting (Fig. 1), indicating potential areas for future improvement to narrow the gap.
0.3
Untormdrpping Star hedtabeee
Figure 2 presents evaluation results on these two bench- marks with model bias. Firstly, static policies, especially Clustering Scorri and Clustering- that depend on eval- uation scores from the training models, do not perform well. Secondly, although random policies do not suffer from model bias, they cannot leverage dependencies across prompts, leading to lower overall performance. In contrast, our RL-based dynamic acquisition policy can effectively exploit the dependencies across prompts even for models that is significantly different from the models it has seen before. However, we notice that the existence of model bias makes the problem harder to solve. Compared to Fig. on the same benchmark, even for our RL-based policy, it takes more acquisitions to achieve the same level of errors as in situations where model bias exists. In practice, continual learning framework, where the NP model and the acquisition policies are jointly adapted to the newly added models, might be necessary. We leave this for future works.
1.34
1:32
Qutter 187
0.00
8.00
1.04
PPromot
Figure 3. Evaluate the cold start problem on MMLU benchmark, where 15 sub- sets are left out as cold start prompts.
Cold Start Problem To evaluate the cold start scenario, create a synthetic benchmark using MMLU by designat- ing 15 subsets as cold start prompts. During the training of the neural process model and the acquisition policies, the evaluation scores on these 15 subsets are not available.
5.1. Ablation Studies
Prediction Model In the main experimental results, we run the acquisition policy to select a subset of prompts
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Table 3. Comparison of different prompt embeddings.
Uniform
C-Embed
C-Score
C-IRT
RL
SFR (4096)
0.005 + 0.000
0.006 1 0.001
0.004 + 0.001
0.003 + 0.001
0.001 1: 0.000
E5 (4096)
0.005 + 0.000
0.006 + 0.001
0.007 + 0.005
0.004 0.001
0.001 +: 0.000
AlpacaEval
BGE-large (1024)
0.006 + 0.001
0.006 + 0.002
0.009 + 0.002
0.007 0.001
0.002 4 0.000
BGE-small (384)
0.009 + 0.002
0.009 + 0.002
0.038 + 0.031
0.015 0.010
0.005 + 0.002
SFR (4096)
0.018 + 0.001
0.020 + 0.004
0.014 + 0.002
+ +
0.013 + 0.000
E5 (4096)
0.018 + 0.002
0.018 + 0.002
+.003
0.016 0.002
0.014 + 0.003
MMLU
BGE-large (1024)
0.029 + 0.003
0.028 + 0.005
0.027 = 0.005
0.023 + 0.006
0.023 + 0.003
BGE-small (384)
0.028 + 0.003
0.023 + 0.006
0.022 + 0.005
0.022 + 0.005
0.022 + 0.002
Table 4. Contributions of auxiliary information and intermediate reward for our RL-based policy.
AlpacaEval
MMLU
Open LLM
PPO +auxiliary infoo
0.004 + 0.002
0.017 + 0.004
0.033 0.010
0.003 + 0.001
0.016 + 0.003
0.029 + 0.005
rewarr
0.001 + 0.000
0.013 1 0.000
0.018 0.001
for acquiring their actual evaluation scores and then use neural process model to predict the scores for the remain- ing prompts. However, an alternative method to estimate benchmark performance is to directly aggregate the acquired evaluation scores without relying on another model for pre- diction. The aggregation computes performance per dataset first and then averages across datasets. Table 2 compares these two estimation methods. The results show that the prediction model generally provides better benchmark per- formance estimation.
intermediate rewards. Table 4 illustrates the contributions of these components. The results clearly show that each component - both auxiliary information and the intermedi- ate rewards- - significantly enhances the acquisition policy, leading to better selection of informative prompts and more accurate benchmark performance estimation.
6. Conclusion
In this work, we present a novel approach for efficient LLM evaluation by leveraging dependency modeling and sub- set selection. Our key contributions include developing a generative model that captures dependencies across eval- uation prompts and handles mixed-type evaluation scores, as well as proposing new subset selection policies based on these dependencies. Extensive experiments on multiple LLM evaluation benchmarks demonstrate the superiority of our RL-based acquisition policy in providing accurate benchmark performance estimation with minimal acquisi- tion budget. Our results show that we can achieve the same level of accuracy while requiring only 35-75% of the evalua- tion prompts compared to random sampling across different benchmarks.
Prompt Embedding Our approach utilizes a sentence embedding model to extract representations for the prompts. These representations are used both to train the neural process model and to build the acquisition policies. For the main results, we use the SFR embedding model (Salesforce SeR--FFeeeeeeeee Limbedding--Mee Masstra) l) (Meng et al., 2024) to extract prompt representations. In Table 3, we present results using several other embedding models: E5, BGE-large, and BGE-small, corresponding (Wang (Xiao al., (Xiao et al.,
to intfloar falfflot//i :tt.e/mmsstaaal---isi---eee et al., 2023), BAAI//ge--arge--e--e 2023), and BAAI 2023), respectively. The results indicate that performance generally improves with more powerful embedding models that better distinguish text inputs?. Thus, exploring powerful embedding models is an important future direction.
Our approach effectively addresses model bias and cold start scenarios, though performance in these challenging settings indicates room for improvement. Future work could ex- plore integrating continual learning frameworks to enhance adaptation new models and prompts. Additionally, lever- aging more sophisticated embedding models and improving uncertainty estimation in the neural process could further boost performance. Our framework provides a foundation for making comprehensive LLM evaluation more accessible and efficient.
Auxiliary Information Our RL-based acquisition policy builds on PPO (Schulman et al., 2017) and leverages the neural process model to provide auxiliary information and
At the time of writing this paper, the aver- age scores from the MTEB English leaderboard. for these four models are: SFR (67.56), E5 (66.63), BGE-large (64.23), and
BGF-smmll 17).
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Impact Statement
Hugginngracet///pen lealln_eeeerbbaarr 2023.
Our work aims to improve the efficiency of LLM evalua- tion, which has several important implications. By reducing the computational resources required for comprehensive model evaluation, mental impact of Al development through reduced energy consumption. This is particularly relevant given the grow- ing concerns about the carbon footprint of large-scale Al systems.
Bowman, R., Angeli, G., Potts, C., and Manning, D. A large annotated corpus for learning natural language inference. arXiv preprint TrXiv:1508.0S320 2015.
method can help decrease the environ-
De Finetti, B. Funzione caratteristica di un fenomeno aleato- rio. In Atti del Congresso Internazionale dei Matematici: Bologna del 3 al 10 de settembre di 1928, pp. 179-190, 1929.
Additionally; more efficient evaluation methods could de- mocratize LLM development by lowering the barrier to entry for researchers and organizations with limited com- putational resources. This could lead to increased diversity in Al research and development, as more groups would be able to rigorously evaluate their models without requiring extensive computational infrastructure or substantial API costs.
Du, J., Grave, E., Gunel, B., Chaudhary, V., Celebi, O., Auli, M., Stoyanov, V., and Conneau, A. Self-training improves pre-training for natural language understanding, arXiv preprint arXiv:2010.021944 2020.
Embretson, S. E. and Reise, S. P. Item response theory. Psychology Press, 2013.
However, acknowledge that easier model evaluation could potentially accelerate the development and deploy- ment of LLMs, which carry their own ethical considerations and societal impacts. We encourage users of our method to carefully consider the broader implications of their work and to maintain high standards for model evaluation, even when using efficient sampling approaches.
Fu, Y., Zhu, X., and Li, B. A survey on instance selection for active learning. Knowledge and information systems, 35:249-2833 280, 283,283,110 2013.
Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D., and Eslami, S. A. Conditional neural processes. In Interna- tional conference on machine learning, pp. 1713- PMLR, 2018a.
Furthermore, while method significantly reduces the number of required evaluations, it is important to note that this efficiency should not come at the cost of thorough model assessment, particularly for safety-critical applications or when evaluating potential harmful behaviors. We recom- mend using our method as part of comprehensive eval- uation strategy rather than complete replacement for thorough testing.
Garnelo, M., Schwarz, J., Rosenbaum, D., Viola, F, Rezende, D. J., Eslami, S., and Teh, Y. W. Neural pro- cesses. arXiv preprint arXiv:1807.0100
2018b.
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee. J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roseher, R. al. A survey of uncertainty in deep neural networks. Artificial Intelligence Review, 56(Suppl 1):1513-1589, 2023.
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Active Evaluation Acquisition for Efficient LLM Benchmarking
A. Training and Deployment Procedure
A.1. Overview
Our approach consists of two key components trained on historical evaluation data:
1. A neural process model that captures dependencies across prompts and predicts unobserved evaluation scores
2. A subset selection policy that determines which prompts to evaluate for new models
A.2. Training Procedure
Given a benchmark leaderboard with models and prompts, we first divide the models into train and test splits. For each model m in the training split, we have access to its complete evaluation scores Ym {1mm J x==1
1. NEURAL PROCESS TRAINING
During training:
For each training iteration:
Sample model m from training split
Randomly partition Yn, into observed set (0() and unobserved set Y
Optimize ELBO objective: maximize log pYy) y
(u)
X)
The trained model predict scores for arbitrary subsets of prompts unseen models
A.2.2. SUBSET SELECTION POLICY TRAINING
We implement and compare several policies:
Parameter-free policies:
Random: Selects prompts uniformly at random
Clustering-Embed: Groups prompts using pretrained embeddings
Clustering-Score: Uses training set scores as features for clustering
Uncertainty Sampling: Selects prompts with highest prediction uncertainty based the neural process model
Information Gain: Maximizes expected information about latent variables using the above neural process model
Combinatorial Optimization: Greedily optimizes prediction accuracy based on the above neural process model
Policies requiring training:
Clustering-IRT: Trains IRT model to learn prompt embeddings
RL-based: Learns policy through interaction with neural process model
A.3. Deployment Procedure
For evaluating model:
1. Policy selects K prompts for evaluation
2. Obtain actual evaluation scores for selected prompts
3. Neural process predicts scores for remaining prompts
4. Combine actual and predicted scores for final performance estimate
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Active Evaluation Acquisition for Efficient LLM Benchmarking
B. Neural Process
For a benchmark X with prompts, X (#n)n= we first use a pretrained embedding model to extract the representations for each prompt. During training, given a model m with evaluation scores Ym we randomly select subset of scores Ym as observed and maximize the log-likelihood for the remaining scores Y/!") based on the equation (3). When y,hh) is too large to fit into memory, we further sample smaller subset from Y((), Due to the inherent permutation invariance of the neural process model, random sampling will not affect the learning of dependencies across prompts. Please see Algorithm for training details of the neural process model.
Algorithm 2 Neural Process Training
Require: Training set [Xm]==) Number of iterations T for t = 1 to T do
2: Sample model m from training set
3: Randomly partition Ym into Y2 and Y:(")
4: Optimize ELBO: L "(mm y x ,x) log p( (io)) ( x)p(zm (Y .X)
(f (Yn .Ym .X)
5:
end for
B.1. Architecture
The neural process model consists of a prior network p(=m y: X), a posterior network ((2m |=((), y-(o), ,X), and a decoder p p(Yil") Zm, Y X) We generally follow the architecture of Attentive Neural Process (Kim et al., 2019), but replace the self-attention layer with more memory-efficient Set Transformer layer (Lee et al., 2018). We also share the same network for both the prior and posterior. Before feeding the prompt embeddings into the prior/posterior network, we use an additional linear layer to reduce the dimensionality of the extracted representations. Similarly, the evaluation scores are passed through a linear layer to increase their dimensionality. We then concatenate the prompt representation with the score representation along the feature dimension and pass the concatenated set of vectors through series of permutation equivariant Set Transformer layers. The outputs are then aggregated across the set elements obtain a feature representation for the entire set. Following Set Transformer approach, we use learned pooling by multihead attention. The set representation is then passed through linear linear to obtain the parameters for the latent distribution, which assume to be Gaussian here. Please see Fig. I(a) for an illustration of the prior/posterior network.
The decoder network p p(Y(1)) Zm;; y:lo) X) uses cross-attention layer produce permutation equivariant representation for each prompt. In this layer, the query is the representation for X, the key is the representation for X()), and the value the permutation equivariant representation corresponding to yr(0) from the prior network. The permutation equivariant representation for each prompt is then concatenated with the prompt representation and the latent vector. These concatenated inputs are processed through a series of Set Transformer layers. The final outputs are then passed through a linear layer to predict the evaluation scores. Please see Fig. 1(b) for an illustration of the decoder network.
B.2. Implementation
In order handle textual prompts, we utilize pretrained embedding model represent each prompt as a TRd vector. During training, since the entire set Y,, might be too large to fit into memory, we randomly sample two non-overlapping subsets from each model as y:(0) and yz(u) respectively. The prior and posterior distributions share the same network, but take
different inputs. The prior takes in a set of x-y pairs from y²(o) i.e., {(Io, Bmo) S NNI.. while the posterior takes in set of x-y pairs from both y((0) and y=("), Following the Attentive Neural Process (Kim al., 2019), we implement the prior/posterior network using self-attention blocks better capture the dependencies across elements. To reduce memory usage,
Set Transformer architecture (Lee et al., 2018), where each set element attends to small set of learnable induced points instead of attending to all other elements directly. The decoder network p(Y/(")) 2mm y=(0), .X) employs cross-attention, allowing each unobserved prompt to attend to the relevant observed prompts. According De Finetti's ssssi Theorem (De Finetti, 1929), the likelihood over set elements y²(x) can be conditionally independent conditioned on the latent variable 2m. However, we still use a Set Transformer (Lee et al., 2018) to better capture the dependencies. Please refer to Appendix B for details of the model architecture.
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Active Evaluation Acquisition for Efficientt LLM Benchmarking
B.3. Mixed-type Evaluation Scores
The above architecture uses linear layer to obtain the representation for the evaluation scores. However, the linear layer not suitable for discrete scores. Instead, benchmark contains mixed-type scores, meaning some datasets report real-valued metrics while others report discrete scores, we additionally include an embedding vector to indicate the metric types.
an Embedding layer to represent the categorical evaluation scores. When
B.4. Hy perpaaraaeeerss
Table B.1 summarizes the hyperparameters used for the neural process model for each dataset. For the HELM-Lite and Chatbot Arena benchmarks, due to their relatively small number of models with evaluation scores, neural process model with set transformer layers can easily overfit the data. Therefore, we use linear layers instead of the set transformer layers. Note that we did not conduct a thorough hyperparameter search. is possible to further improve the results with optimized hyperparameters.
Table B.I. Hyperparameters for the nucral process model.
AlpacaEval
MMLU
Open LLM
HELM-Lite
Chatbot Arena
representation dimension for xr representation dimension for y feature dimension for permutation equivariant layer
16
16
16
16
16
16
16
16
32
32
32
32
16
number of permutation equívariant layers for encoder number of permutation equivariant layers for decoder number of attention heads number of induced points latent dimension
1
1
N/A
N/A
16
16
8
N/A
N/A
16
32
32
16
8
C. Combinatorial Optimization based Acquisition Policy
Given the model p(Y:(") yr(o) X), static acquisition policy can be derived by searching over the training set to find the optimal subset of prompts that gives the most accurate prediction of the remaining prompts. This is a typical combinatorial optimization problem, which is NP-Hard. Here, we employ a sequential approach that selects one prompt at a time until K prompts are selected. Starting from an empty set o O, the next prompt E u == (I....,NN \ prediction error over the training set, i.e..
is chosen to minimize the
i = arg min Ey,,.pp E ( N'E
wptr! (rse") ()) ( y(u')
(C.1)
where o' = ou (U'))()) {i'} and v' = a (P). We estimate the expectation by Monte Carlo sampling. For notation simplicity, the above equation computes the mean squared error on prompts u': however, in practice, different datasets may use different metrics. Additionally, these differences may be weighted depending on the dataset size. Please refer Algorithm 3 for pseudo-code of the selection process. Note that this approach has complexity of O(K MN), which could be prohibitive when the benchmark is large.
D. Reinforcement Learning based Acquisition Policy
The acquisition policy determines the next prompt to acquire its evaluation score based on the current state, which includes the prompts (o) and their scores y= that have already been acquired. We further incorporate the candidate prompts (") into the policy inputs, i.c., P(i ( .X), the policy has access to the action space. Including the candidate prompts in the inputs is crucial in the cold start setting since the action space differs between training and testing, Similar to the neural process model, the policy network employs two linear layers to obtain representations for both the prompts and the evaluation scores, which are then concatenated along the feature dimension. For the candidate prompts without available evaluation scores. we use special embedding vector. Then, a permutation-invariat network processes the set of concatenated representations and outputs a aggregated representation for the entire set. We utilize the Set Transformer architecture for the permutation invariant network. Two branches of linear layers are added on top of the set representation for actor and critic, respectively. The actor branch outputs vector with the same dimensionality as the prompt representations.
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Permutation Equivariant Representation
(x, J.
}M
X()
Prompt
Eval Score
Embedding Model
Prompt Embedding
Cross Attention Layer
Linear Layer
Linear Layer
Concat
Latent Vector
Concat
Transformer Layer
L
Parmutation Equivariant
Set Transformer Layer
L
Reprosantation
Poaling
Linear Layer
Linear Layer
Predicted Score
(a) Prior/Posterior
(b) Decoder
Figure B.I. The architecture of the neural process model.
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Algorithm 3 Statie Evaluation Acquisition via Combinatorial Optimization
Require: Acquisition budget K, Training set Dtrain. Number samples S, Neural Process p o= 0, u [N.
2: while [o| K do
3: L {}
4: for i' u do
5:
='' n' u fd'}
6: Sample S predictions (1 (Prh))
i
from p p((1a)) yrlo), ,) for each model m
7: L Deraiin XxS Σ Perainl ===1
mm=1 E
m. Y: 112
8:
end for
9: arg min L|(
10:
o U {i}, 1= u {1}
11: end while
The probability of selecting a prompt is proportional to the inner product of the output vector and the prompt representations. To prevent the policy from selecting duplicate prompts, the probability the already selected prompts is manually set to zero. The critic branch outputs a scalar indicating the value estimation for the current state. Table D. summarizes the hyperparameters used for the policy network and PPO training process. We did not conduct hyperparameter optimization and used the same set of hyperparameters for all datasets. Further improvements are likely possible with hyperparameter optimization tailored to each dataset.
Table D.I. Hyperparameters for RL-based acquisition policy.
representation dimension for æ representation dimension for y
16
16
feature dimension for permutation equivariant layer
32
Policy Network
number of permutation equivariant layers number of linear layers for actor number of linear layers for critic
1
1
advantage X discount factor Y PPO clip range entropy coefficient
0.95
0.99
PPO
[0.8, 1.2]
0.0
E. Cold Start Problem
In the cold start setting, the benchmark is expanded with new prompts for which evaluation scores are initially available for any model. That is, the original benchmark X = { have evaluation scores Y,. [y for M models, while a set of new prompts X' = {z, Ja do not have any evaluation scores.
To enable the neural process model generalize to the newly added prompts, we propose a semi-supervised training procedure, where the new prompts are treated as unlabeled data. During training, we optimize the log-likelihood (3) for X and Yn. Simultancously, we predict the evaluation scores for the new prompts X' based on the current trained model. When the prediction is sufficiently accurate, meaning the uncertainty is lower than predefined threshold, add the predicted scores as synthetic training data to optimize the ELBO (3).
The RL policy in the cold start setting follows similar architecture to Sec. D. To help the policy generalize to unseen prompts, we use the learned prompt representations from the neural process model and keep them fixed throughout the training process. Additionally, we found that entropy regularization over the actor distribution aids generalization, which is set to 0.001 in our experiments.
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Active Evaluation Acquisition for Efficient LLM Benchmarking
F. Experiments
F.I LLM Leaderboard
We conduct experiments on popular LLM benchmarks:
HuggingFace Open LLM Leaderboard (Beeching et al., 2023) consists of 6 datasets with a total of 28,659 prompts. Evaluation scores include both binary accuracy and real-valued probabilities. We collect evaluation scores for 2,084 models and select 1,000 models for training based on their evaluation date. The most recently evaluated models are used for testing, simulating the real-world scenario.
MMLU (Hendrycks et al., 2020) contains 57 datasets with a total of 14,042 multiple choice QA problems on different subjects. Evaluation scores are all binary accuracy. We collect evaluation scores for the same models from the Open LLM Leaderboard.
HELM-Lite (Liang et al., 2022) include 10 datasets (each possibly containing several sub-datasets) with a total of 13,021 prompts. Evaluation scores include both binary exact match scores and real-values metrics such as FI and BLEU. We collect evaluation scores for 33 models and randomly select 23 models for training since the evaluation does not have dates.
AlpacaEval 2.0 (Li et al., 2023) contains 805 prompts. For each model, the generations are compared to those of GPT-4 to compute the win rate. Although this benchmark is relatively small, it requires an expensive GPT-4 based judge, so reducing the number of API calls can significantly reduce the total evaluation cost. We collect evaluation scores for 130 models and randomly select 70% for training.
Chatbot Arena (Zheng et al., 2024) is popular human-annotated benchmark, where annotators interact with two anonymous models using the same prompts and declare a winner. We use the pairwise comparisons evaluated on the 80 MTBench prompts (Zheng et al., 2024). Although this benchmark relatively small, human evaluation expensive, so further reducing the evaluation prompts could lower costs. The annotations include comparisons over multiple turns, but we only the annotations for the first turn here. Unlike other benchmarks where each model directly receives an evaluation score, this benchmark evaluates each pair from set of 6 models. To create the train-test splits, randomly select one of the six models, and all pairs that involve the selected model are included in the test split. Note that not all 80 prompts are annotated for each model pair. While our neural process model can handle missing data, the acquisition process must acquire the true score for any prompt the policy selects. To address the missing data during acquisition process, we use the trained neural process to predict the missing evaluation scores. We report win rate for this benchmark.
F.2. Evaluation Procedure
For model m' to be evaluated, the acquisition policy determines a subset of prompts X to acquire the true evaluation scores. The neural process model p(Y((v) y X)) predicts the evaluation scores for the remaining prompts. The benchmark performance is then estimated based on these predicted scores. For benchmarks with only one dataset, the benchmark performance is the average over all examples. For benchmarks with multiple datasets, the benchmark performance is averaged over the performance of each dataset. For example, the HuggingFace Open LLM Leaderboard consists of 6 datasets, so the benchmark performance is the average of the performance on these 6 datasets. The MMLU dataset further contains 57 subsets, so its performance is the average over these 57 subsets. For Chatbot Arena, we report win rate as the benchmark performance. For final evaluation results, we compute the absolute difference between the predicted benchmark performance and the real benchmark performance for each model in the test split and report the average absolute error over all models in the test split.
E.3. Additional Results
Table F.1 presents the benchmark performance estimation errors for various acquisition policies across different LLM benchmarks. We conduct experiments with 3 random seeds for each benchmark and report the average estimation error and standard deviation under the specified acquisition budget. Prompt embeddings are obtained using the SFR embedding model. For the AlpacaEval and Chatbot Arena benchmarks, stratified random sampling is equivalent to uniform sampling since they only contain one dataset. Combinatorial optimization and information gain based policies are too expensive to run for M-LM-Lite, HuggingFace Open LLM Leaderboard, and MMLU due to the large number of prompts each benchmark.
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Table F.I. Benchmark performance estimation error each I.I.M benchmark. Lower is hetter.
AlpacaEval
HELM-Lite (K=200)
Open LLM
MMLU
Chatbot Arena
(K=100)
(K=200)
(K=100)
(K=40)
Uniform
0.005 + 0.000
0.038 +: 0.005
0.022 + 0.002
0.018 + 0.001
0.052 + 0.010
S-Rand
0.035 + 0.012
0.030 = 0.003
0.017 + 0.002
C-Embed
0.006 + 0.001
0.051 0.010
0.021 + 0.002
0.020 + 0.001
0.052 0.014
C-Score
0.004 = 0.001
0.010
0.031 = 0.003
0.014 0.002
0.054 0.004
C-IRT
0.003 = 0.001
0.044
0.026 0.001
0.015 - 0.001
0.057 = 0.002
Comb-Optin
0.006 = 0.003
0.065 - 0.012
Uncertainty
0.011 + 0.001
0.055 0.013
0.063 + 0.015
0.050 + 0.003
0.035 + 0.003
LatentInfoGain
0.010 + 0.003
0.066 0.025
RL
0.001 + 0.000
0.030 + 0.005
0.018 + 0.001
0.013 + 0.000
0.034 + 0.006
The RL-based acquisition policy consistently achieves the lowest error across all benchmarks, indicating its superior ability to select informative prompts and accurately estimate benchmark performance. The stratified random sampling performs similarly to the uniform sampling, and these random acquisition policies generally are competitive, particularly because they are efficient and do not rely on any other models to determine the prompt selection.
The Cluster-Embed policy does not perform any better than the random selection, suggesting that the similarity in prompt embedding does not always correlate with the similarity in the evaluation scores. Utilizing evaluation scores for clustering shows mixed results. The Clustering-Score policy outperforms Clustering-Embeed on AlpacaEval and MMLU but underper- forms on HELM-Lite, Open LLM and Chatbot Arena benchmarks. Clustering based on IRT features generally provides better performance estimation since these features are learned to reflect the evaluation scores.
The combinatorial optimization based policy does not perform well, even on the two small benchmarks where it computationally feasible. We attribute this to potential distribution shift between the models used for training and those used for testing, suggesting that the static policy optimized on training models does not generalize well new models during testing.
The uncertainty sampling based acquisition policy does not perform well across all benchmarks. Theoretically, the uncertainty sampling method requires good estimation of the aleatoric uncertainty to perform well. However, in practice, the uncertainty from the neural process model combines the aleatoric and epistemic uncertainties. Quantifying and decomposing the aleatoric and epistemic uncertainties is an active research ares in machine learning (Gawlikowski et al., 2023; Wimmer et al., 2023; Hüllermeier & Waegeman, 2021), which we leave for future work to explore for our AEA application. Similarly, the information gain based acquisition policy also requires accurate uncertainty estimation, which is challenging, especially with scarce training data on AlpacaEval and Chatbot Arena benchmarks.
F.4. Further Analysis of the Results
From the main results in Figure 1, we found that our approach achieves exceptional estimation on AlpacaEival with near 0% absolute error, while other benchmarks the error is obviously above 0. To better understand the factors affecting evaluation score prediction accuracy and the varying performance across different benchmarks, we conducted detailed analysis using the HELM-Lite benchmark. We chose HELM-Lite for this analysis due to its diverse datasets covering different types of tasks and metrics. To isolate the impact of prompt selection policy, we performed this analysis using 50 randomly selected prompts as conditioning to predict the evaluation scores.
It's worth noting that prompts that are inherently difficult for language models, resulting in consistently low scores, do not necessarily translate high prediction error for the evaluation scores. For example, if no model can solve particular prompt, its evaluation score will always be zero, making it relatively easy to predict.
Factors Affecting Prediction Accuracy For dataset with N prompts evaluated on M models from the training set, with evaluation scores denoted as {ymn m== we analyzed the following factors that might affect the evaluation score prediction:
Metric Types: We hypothesized that discrete metrics (c.g., exact match accuracy) would be harder to predict than continuous metrics (e.g., BLEU scores).
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Table F.2 shows the correlation between these factors and prediction error across subsets from Active Evaluation Acquisition for Efficient LLM Benchmarking
Prompt Diversity: A dataset with diverse prompts is potentially harder for LLM. The prompt diversity is measured using the average pairwise cosine similarity of prompt embeddings.
Task Difficulty: We estimate the task difficulty as minus the average evaluation scores across all models, i.e., MN Em,n Mmns where higher values indicate more challenging tasks.
Score Diversity: A dataset where the score distribution has high variance can potentially lead to higher prediction error. The score diversity is calculated as the variance of evaluation scores within the dataset.
Task Informativeness:
We estimate the informativeness of each prompt as the variance its evaluation scores on all
models, then the task informativeness is averaged over prompts: Σn Var, (ymn), measuring how discriminative each prompt is across different models.
Evaluation Variability: Calculating the variance of the mean scores across prompts is another way to quantify the variability or diversity in the task: Var, ( J7 2mm Mmn).
Results and Analysis H-LttLite:
Table F.2. Correlation between dataset characteristics and prediction error
Factor
Spearman
Pearson
Metric Type
0.783
0.759
Prompt Diversity
-0.722
-0.719
Score Diversity
0.216
0.366
Task Informativeness
0.720
0.781
Evaluation Variability
0.885
0.842
Task Difficulty
-0.195
-0.138
Key findings from our analysis:
1. Metric Type Impact: The strong correlation (0.78) confirms our hypothesis that discrete metrics are more challenging to predict than continuous ones. This helps explain why benchmarks with primarily discrete metrics (e.g., MMLU) show higher prediction errors compared those with continuous metries.
2. Prompt Diversity:
Interestingly, higher prompt diversity correlates with lower prediction error (-0.72). This counter- intuitive finding suggests that while diverse prompts may be challenging for LLMs to answer, they might actually provide richer signals for predicting evaluation scores. This also aligns with the analysis that harder task for LLM does not necessarily mean harder evaluation score prediction. This is further supported by the negative correlation between Task Difficulty and prediction error.
3. Task Informativeness: Task Informativeness has a high correlation with prediction error, expected, since prompts with high variance in evaluation scores are inherently harder to predict accurately.
This analysis helps explain the varying performance across different benchmarks. For instance, AlpacaEval's near-zero prediction error can be attributed to several factors:
Its relatively small size (805 prompts) compared to other benchmarks makes dependencies easier to capture
continuous win-rate scores from a logistic regression model are inherently smoother than discrete metrics
The evaluation scores come from single consistent source (GPT-4 evaluator)
In contrast, benchmarks like MMLU and HELM-Lite show higher prediction errors due to their larger size, predominantly discrete metrics, and more diverse evaluation criteria across different subsets.
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Table F.3. Prediction enror by metric type
Metric Type
Prediction Error
Binary Real-valued
0.1055
0.0334
Table F.4. Prediction error by specific metric
Metric
Prediction Error
BLEU-4
0.0302
Fl Score
0.0386
Exact Match
0.0777
Quasi Exact Match
0.0957
Final Number Exact Match
0.1134
Math Equivalence Chain-of-
Saain-of-Thore Thought
0.1367
Impact of Metric Types To further investigate the impact of metric types, we analyzed prediction errors across different metrics. Tables F.3 and F.4 show detailed breakdowns:
The results clearly demonstrate that:
Binary metrics have significantly higher prediction errors (0.1055) compared to real-valued metrics (0.0334)
Among specific metrics, continuous measures like BLEU-4 (0.0302) and Fl Score (0.0386) show lower prediction errors
Complex discrete metries, particularly those involving mathematical reasoning (Math Equivalence Chain-of-Thoughh 0.1367), present the highest prediction challenges
F5. Discussion on Random Sampling as Baseline
Random sampling serves as a compelling bascline for efficient benchmarking due to several attractive properties:
Simplicity: Implementation requires no additional models or complex selection strategies
Statistical Guarantees: Supports standard statistical analysis and uncertainty quantification
No Cold Start: Naturally handles new prompts without requiring historical evaluation data
Interpretability: Results are easily understood and trusted by benchmark users
Our experiments show that random sampling achieves competitive performance compared to more sophisticated methods. This aligns with observations in the active learning literature, where simple random sampling often performs surprisingly well when the labeling budget is large (Lu et al., 2023; Hacohen et al., 2022; Tifrea al., 2022). Similar phenomena have been observed in experimental design and bandit problems (Jamieson & Jain, 2018; Pacchiano et al., 2024), suggesting this is a general principle rather than specific to our setting.
However, closer examination of our results reveals that our RL-based policy can achieve the same estimation accuracy with significantly fewer prompts:
This efficiency gain is particularly meaningful in the context of L.LM evaluation, where each prompt evaluation:
Requires significant computational resources
May incur API costs for closed models
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Table F.5. Comparison of prompts required achieve similar estimation errors
Dataset
Uniform Sampling
RL Policy
Prompts
Error
Prompts
Error
AlpacaEval
100
0.0051+0.0000
0.005110.0000
MMLU
100
0.0179#0.0011
35
0.0172+0.000 0002
HELM-Lite
200
0.0376+0.000 0060
50
0.0350=0.0000
OpenLLM
200
0.0225#0.00%
100
0.0223#0.005
MT-Bench
40
0.0518+0.0127
23
0.0506+0.0118
Contributes to environmental impact through energy consumption
The choice between random sampling and our method presents several trade-offs:
1. Sample Efficiency vs. Complexity: Our method achieves better sample efficiency but requires maintaining additional models and more complex implementation.
2. Statistical Guarantees vs. Empirical Performance: Random sampling provides clear statistical guarantees, while our method's theoretical properties are less well-understood despite strong empirical performance.
3. Immediate Deployment Training Requirements: Random sampling be deployed immediately, while method requires initial training on historical evaluation data.
In practice, the choice between methods should be guided by specific requirements:
When evaluation costs are high and minimizing the number of prompts is crucial, our method offers substantial benefits
When statistical guarantees or immediate deployment are prioritized, random sampling may be more appropriate
For large-scale evaluations where even small reductions in prompts translate to significant cost savings, the added complexity of our method may be justified
These considerations suggest that both approaches have their place in the LLM evaluation ecosystem. Random sampling serves as reliable, interpretable baseline, while our method offers a more sophisticated alternative when maximum sample efficiency is desired. Future work could explore hybrid approaches that combine the statistical guarantees of random sampling with the efficiency gains of learned selection strategies.
F.6. Comparison with Uncertainty Estimation Baselines
natural alternative to our RL-based acquisition policy would be to use uncertainty estimates from the model's generations to determine which prompts to evaluate. To validate our approach, we compared our RL-based policy with two common uncertainty estimation baselines:
Generation Perplexity: Using the log-likelihood of the model's generation an uncertainty estimate
Semantic Entropy (Kuhn al., 2023): Computing entropy over semantic clusters of multiple generations using a pretrained NLI model
We conducted experiments on three recent language models (Mistral-7B. Mixtral-8x7B, and Gemma-7B) evaluating their performance on the AlpacaEval benchmark. For each model, we compare different methods for selecting prompts to evaluate, while using the same neural process model to predict scores for the remaining prompts.
As shown Table F.6, our RL-based acquisition policy consistently outperforms both uncertainty estimation baselines across all models and selection budgets. We hypothesize that this superior performance stems from two key factors:
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Active Evaluation Acquisition for Efficient LLM Benchmarking
Table F.6. Comparison of acquisition policies on AlpacaFval benchmark, Values show absolute error in henchmark score estimation under
different selection budgets.
Model
Method
Selection Budget
10
20
50
100
Perplexity
0.0162
0.0156
0.0103
0.0088
Mistral-7B
Semantic Uncertainty
0,0122
0.0175
0,0126
0,0150
RL (ours)
0.0012
0.0008
0.0006
0.0006
Perplexity
0.0025
0.0053
0.0058
0.0076
Mixtral-888
Semantic Uncertainty
0.0095
0.0145
0.0132
0.0202
RL (ours)
0.0019
0.0003
0.0003
0.0002
Perplexity
0.0090
0.0190
0.0136
0.0147
Gemma-7B
Semantic Uncertainty
0.0148
0.0139
0.0144
0.0169
RL (ours)
0.0013
0.0007
0.0009
0.0004
1. Aligned Uncertainty Estimation:
The RL policy is trained jointly with the neural process model, allowing it to better align its uncertainty estimates with the neural process's 's predictive uncertainty. contrast, inference- uncertainty methods may capture the model's inherent uncertainty but fail identify cases where the neural process predictions would be most uncertain or inaccurate.
2. Sequential Decision Making: The RL policy can adapt its selection strategy based on previously observed scores, while uncertainty-bast methods make independent decisions for each prompt.
It's worth noting that these inference-dependent uncertainty methods are impractical for efficient benchmarking in practice. They require running inference on all prompts (and sometimes multiple times per prompt), which increases rather than decreases the overall evaluation cost. In contrast, our RL-based policy makes selections based solely on prompt embeddings and previously observed scores, maintaining the efficiency benefits of our approach.
G. Computational Complexity Analysis
When comparing the computational complexity of our method to full benchmark evaluation, there are two primary components to consider: (1) the cost of acquiring evaluation scores for the selected subset of prompts, and (2) the computational overhead from running the neural process model and acquisition policies.
Evaluation Cost Savings The main computational savings come from the dramatic reduction in the number of prompts requiring evaluation. Our experiments demonstrate that accurate performance estimation can be achieved with only a small fraction of the total prompts. Specifically, our method requires less than
Computational Overhead While our method introduces some additional computation through the neural process model and acquisition policies, this overhead is minimal compared to the cost LLM inference. The bottleneck evaluation cost lies in LLM inference, which typically requires high-end computational resources and often involves models with billions of parameters. In contrast, our neural process model and acquisition policies are implemented with lightweight architectures (2-3 linear layers in experiments) that be executed efficiently on modest hardware with minimal computational resources.
In conclusion, by reducing the number of required LLM evaluations by orders of magnitude, our method offers substantial computational savings compared to full evaluation, even when accounting for the additional overhead of running our models. The lightweight nature of our approach ensures that the computational benefits scale well with increasing benchmark sizes and model complexities. This makes our method particularly valuable for large-scale benchmarks where comprehensive evaluation would be prohibitively expensive.
22


Reviews:
[{"review_id": "GmsO2w5lEx", "reviewer": "Reviewer_wFjY", "summary": "This paper proposes a large language model (LLM) evaluation method, which considers dependency modeling and subset selection to improve efficiency. The authors develop a model that captures dependencies across evaluation prompts and propose subset selection policies based on these dependencies. Extensive experiments on multiple LLM evaluation benchmarks demonstrate the superiority\nof the proposed method in providing accurate performance estimation with minimal acquisition budget.\n\n## update after rebuttal\n\nThe authors' rebuttal solves most of my concerns. I regard this paper as a weak accept case and maintain my score.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for their thoughtful assessment of our work. Below, we address the key points raised:\n\n## Concern about Subset Selection and Fairness Across Models\n\nThe 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:\n\n1. 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.\n\n2. 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.\n\n3. 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.\n\n## Adaptation to New Models and Benchmarks\n\nThe reviewer asks about our method's ability to adapt to new models and benchmarks, which is indeed crucial for LLM evaluation:\n\n1. 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.\n\n2. 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.\n\n3. 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.\n\n## Missing Reference on Active Evaluation\n\nWe 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.\n\nOur 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.\n\n## Additional Comments\n\nWe note the reviewer's positive assessment of our paper's strengths, including:\n- The importance of the research problem\n- The soundness of our proposed methods\n- The clarity and organization of the paper\n\nThese align with our goal of addressing a practical challenge in LLM development while maintaining scientific rigor.\n\n## Conclusion\n\nWe 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.\n\nIn the revised version, we will:\n1. Further clarify how our approach maintains fair comparability between models\n2. Incorporate the suggested reference and discuss its relevance\n3. Expand our discussion on adaptation to new models and benchmarks\n\nThank you for the opportunity to address these points and strengthen our paper.", "claims_and_evidence": "The claims made in the submission are supported by clear theoretical and empirical evidence.", "methods_and_evaluation_criteria": "The proposed method and evaluation criteria almost make sense for the problem. However, there is a potential risk that the selected subset for different groups of LLMs may vary. Thus, the comparison result of two LLMs' performance may be affected by the group of LLMs in the training dataset, which seems unnatural.", "theoretical_claims": "I have checked most of the theoretical part in this paper and found no obvious errors.", "experimental_designs_or_analyses": "The experimental designs or analyses are almost sound.", "relation_to_broader_scientific_literature": "Compared with the works in the broader scientific literature, the key contributions of this paper is to improve LLM evaluation efficiency via dependency modeling and subset selection.", "essential_references_not_discussed": "There are some important papers about active evaluation of natural language generation (NLG), which should be properly discussed in this paper. For example, [1] investigates active learning in the related evaluation setting (i.e., pairwise comparison).\n\n[1] Active Evaluation: Efficient NLG Evaluation with Few Pairwise Comparisons. ACL 2022", "other_strengths_and_weaknesses": "Strengths:\n\n1. The research problem about efficient LLM evaluation is interesting and realistic, because the cost of LLM evaluation increases quickly since the model parameters become larger.\n2. The proposed method is sound and convincing with dependency modeling and subset selection.\n3. This paper is well-organized and overall easy to follow.\n\nWeaknesses:\n\n1. I wonder whether the proposed method can effectively adapt to new models and benchmarks, since the core challenge of LLM evaluation compared with traditional NLG tasks' evaluation is the high requirement of generalization ability. The authors should add more theoretical or empirical analysis about this point.", "other_comments_or_suggestions": "None.", "questions_for_authors": "I have included my questions in other parts of the review.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zEpjYcQYkK", "reviewer": "Reviewer_srP5", "summary": "The paper focuses on LLM efficient evaluation , that is, estimating overall performance based on a subset of data. The authors first model dependencies across evaluation prompts using neural processes, then analyze various selection methods and propose a RL-based method.  Additionally, for the cold start problem, the authors propose a semi-supervised approach to solve it. Finally, they compare various methods on five benchmarks and conduct a comprehensive analysis.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for their thorough evaluation of our work and the positive recommendation. Below, we address the points raised:\n\n## Practical Applications of Efficient Evaluation\n\nWe 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:\n\n1. Enabling more frequent intermediate evaluations during training, allowing earlier detection of issues\n2. Supporting more extensive hyperparameter tuning by reducing evaluation costs per configuration\n3. Facilitating rapid comparisons between model variants during research iterations\n4. Reducing API costs for closed-source models during the development phase\n5. Decreasing environmental impact through reduced compute requirements\n\n## Implementation Workflow for New Models\n\nRegarding 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:\n\n1. **One-time setup**: Training the neural process model and acquisition policy on historical benchmark data\n2. **Per-model evaluation**: Sequentially selecting prompts, obtaining scores, and predicting remaining scores\n\nOnce this setup is complete, the evaluation process for each new model adds minimal computational overhead compared to the actual LLM inference costs.\n\n## Complexity vs. Random Sampling Trade-off\n\nWe 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:\n\n- Organizations conducting ongoing LLM development and benchmarking\n- Evaluations of large or expensive models where each prompt evaluation is costly\n- Scenarios requiring adaptation to new model families or previously unseen prompts\n\nThe 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.", "claims_and_evidence": "Yes, the experiment results support their claims well.", "methods_and_evaluation_criteria": "Yes, the methods, baselines and the selected benchmark are appropriate.", "theoretical_claims": "Yes. The reasoning process of ELBO in section 3.1 is correct.", "experimental_designs_or_analyses": "Yes. The baselines selection in section 3.2 is comprehensive, including random policy, clustering, IRT, uncertainty sampling, and so on.", "relation_to_broader_scientific_literature": "The work focus on efficient evaluation of LLM. Most important related work have been discussed in this paper, such as CAT and Clustering- IRT", "essential_references_not_discussed": "You should cite the \"Anchor Points: Benchmarking Models with Much Fewer Examples\" published in ACL 2024, although you have discussed their method: Clustering", "other_strengths_and_weaknesses": "Strengths:\n- The experiments and analyses were both conducted comprehensively.\n- The paper is well-structured.\n\nWeaknesses:\n- Although RL-based methods have achieved better performance than random sampling, it is more complex as they require evaluation data history, training a VAE model, and training an RL model. By comparison, the random sampling is still a strong baseline. More importantly, it is simple and efficient. So, whether the RL-based method is more suitable for practical applications remains uncertain.", "other_comments_or_suggestions": "A suggestion is that you should highlight the practical applications of efficient evaluation in the introduction, for example, it could be used for rough testing during the model development process, etc.", "questions_for_authors": "Please explain how to evaluate a new model using your method from scratch, given a benchmark that already contains test results for some models. Specifically, outline each step involved and estimate the workload required, such as training models. Additionally, discuss whether, given this workload, researchers would be more inclined to adopt your method or simply use random sampling.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xO9svVqjB8", "reviewer": "Reviewer_NcUZ", "summary": "This paper introduces a novel RL-based method to LLM's benchmarking evaluation. From the aspects of efficiency and accuracy, they improve the accuracy in evaluation and also lower the computation overhead in the process of evaluation. They are inspired by active learning and propose their approach by modeling the dependencies across different samples. Their experimental results show their method's performance including accuracy and robustness, and also demonstrate their contribution to efficiency in LLM's evaluation field.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "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:\n\n## Evaluation Metrics and Result Interpretation\n\nThe 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.\n\nRegarding 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.\n\nFor 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.\n\n## Statistical Significance and Random Seeds\n\nWe 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.\n\n## Neural Process Background\nWe appreciate the suggestion to provide more background on neural processes. In the revised version, we will expand Section 3.1 to include:\n1. A more intuitive explanation of how neural processes model stochastic functions\n2. A brief comparison with related approaches like Gaussian Processes\n3. A clearer explanation of why neural processes are particularly well-suited for capturing dependencies across evaluation prompts\n\nThis additional background will make our methodology more accessible to readers who may be less familiar with these techniques.\n\nWe 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.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "The methods make sense. Because the active learning can truly improve the efficiency and keep the accuracy. They include the active learning and RL-based method to model the dependencies to achieve their goals. Also their method is based on the neural process and we can find the relevant literature to support the parts in method. And the method is consistent with their motivation.\n\nFor their evaluation criteria, I think it's good. For absolute error, it can show method's effectiveness. But I am not sure whether it's the best. Or maybe it's not enough. For example, they could include the running time to prove they improve the efficiency. And from the numbers of  absolute error, different results are not very distinguishable. Is there any other better criteria. Or maybe they could show the metric they use is most used often.", "theoretical_claims": "I think their theoretical parts are correct. They didn't put any theorems/lemmas. Just from the equations in paper, I think they are clear and not wrong. For example, from the equation7-9, they are clear to define a reward way in RL to show their novel ideas in method. So far, I didn't see any wrong things in theoretical parts.", "experimental_designs_or_analyses": "Yes I did. I think their experiments are good. But there are some concerns: 1) We can see some big std in some figures and they run the code using 3 different random seeds. I think it would be better to run more times, like 5 different random seeds to lower the std of baselines.\n2) Still about the absolute error, the paper did not write why the metric is good. Because the numbers of all results are so small, I can't conclude how powerful their method is. Maybe they can include some instructions on why they use the metric.", "relation_to_broader_scientific_literature": "They introduced active learning to LLM's evaluation. I think their work can inspire later related research areas. In the future, there may be more works to lower the computation cost in LLM's evaluation and keep the performance at the same time. Also in the other fields related to LLM, how to improve the accuracy of other LLM's tasks and also improve the efficiency.", "essential_references_not_discussed": "I think the necessary references are included in the method section and results part.", "other_strengths_and_weaknesses": "Other Strengths: They present their paper in a clear way to show the pipeline and motivation along with corresponding supporting results. Their algorithms'  pseudo and figures in paper are clear and great.\nOther Weaknesses: If they could introduce the neural process in the preliminary more, it would be better.", "other_comments_or_suggestions": "I don't have other comments.", "questions_for_authors": "Could you run more different random seeds to decrease the variance of baselines? That would show your method's performance better. Current some of figures are not so clear due to big shaded areas caused by variance.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MKQwzRrOEQ", "reviewer": "Reviewer_kxi9", "summary": "The paper presents an approach to improve the efficiency of evaluating large language models (LLMs) by selecting a subset of evaluation prompts through a learned policy. The authors claim that their RL-based approach significantly reduces computational cost while maintaining accuracy.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer's feedback and concerns. Below we address the specific points raised:\n\n## Evidence for Prompt Correlations\n\nThe 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.\n\n## Benchmark Selection and Generalizability\n\nRegarding 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:\n- AlpacaEval (win rate against reference models)\n- HELM-Lite (multiple tasks and metrics)\n- OpenLLM Leaderboard (multiple-choice QA)\n- MMLU (subject-specific academic tests)\n- Chatbot Arena (human preference judgments)\n\nThese 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.\n\n## Computational Efficiency of RL-based Approach\n\nThe 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:\n\n1. The one-time training of our policy and neural process model is computationally inexpensive (typically 2-3 hours on a single GPU)\n2. During inference, our policy network adds negligible computational overhead compared to the cost of running LLM inference\n3. The savings from reducing the number of required evaluations (65-92% fewer) vastly outweigh any overhead from our method\n\nOur approach makes large-scale LLM evaluation significantly more accessible, especially when evaluating resource-intensive models or when using costly API calls for closed models.\n\n## Statistical Significance\nRegarding 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.\n\nOur 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.\n\n## Visual Representation\n\nWe 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.\n\nWe 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.", "claims_and_evidence": "1.  The author assumes that evaluation scores across prompts are highly correlated, but no strong evidence is provided to support this assumption.\n2. Only 5 benchmarks are selected. This could lead to misleading conclusions about the model's generalized performance.", "methods_and_evaluation_criteria": "1. The proposed RL-based technique seems computationally expensive. Therefore, further justification is required regarding its real-world applicability. \n2. Missing proof that the RL-based policy generalizes better than simpler baselines.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "1. The experimental section lacks the statistical significance test. \n2. Only 5 benchmarks are selected. This could lead to misleading conclusions about model's generalized performance.", "relation_to_broader_scientific_literature": "1. Somewhat useful but adding more benchmarks and models would have been better.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths: Addresses an important problem to reduce LLM evaluation cost. Also, a well motivated paper. \nWeaknesses: Only 5 benchmarks are selected. This could lead to misleading conclusions about the model's generalized performance. Also, the RL-based technique seems computationally expensive.", "other_comments_or_suggestions": "Would have been much better for the readers if the author demonstrates the task and the proposed methods using a figure.", "questions_for_authors": "1. Why only 5 benchmarks are selected and how do they ensure the generalized effectiveness of the proposed method?\n2. Why no figure is presented to demonstrate the task and the proposed method?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "viwFT300fb", "reviewer": "Reviewer_GY8p", "summary": "The paper deals with efficient evaluation and offers a new way to dynamically choose which examples to use, per model. It shows great results, the improvements are very clear, the novelties are too, the writing is also mostly easy to follow.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank the reviewer for their detailed feedback and strong endorsement of our work. We address each point below:\n\n## Terminology: \"Prompts\" vs \"Examples\"\nWe 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.\n\nWe will revise the paper to clarify this terminology early on to avoid confusion with other types of prompt-related work.\n\n## Definition of Evaluation Scores (Y)\n\nWe 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.\" \n\nWe will improve clarity by providing a more explicit definition earlier in the paper.\n## Probability Formulation of Eq.1\n\nWe 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.\n \nEquation (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.\n\nIn our revision, we will add an intuitive explanation of this formulation explaining the intuition behind our probability formulation to avoid confusion.\n\n## Method Overhead and Implementation\n\nThe 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:\n\n1. The one-time training of the neural process model and acquisition policy takes 2-3 hours on a standard GPU\n2. The policy network is lightweight, consisting of only 2-3 linear layers that execute in milliseconds\n3. The 43-92% reduction in required evaluations vastly outweighs this overhead\n\nIn 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.\n\n## Distinction from Active Testing and Active Learning\n\nWe 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:\n\n1. Active Learning typically selects examples to label for training a model\n2. Active Testing focuses on reducing the labeling cost for evaluating model performance \n3. Our Active Evaluation Acquisition (AEA) specifically targets reducing the number of prompt evaluations needed during LLM benchmarking\n\nWe 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.\n\n## Figure Labeling\n\nWe 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.\n\n## Typo on line 207\n\nThank you for catching this. We will correct it in the revised version.\n\nWe 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.", "claims_and_evidence": "Well supported", "methods_and_evaluation_criteria": "Well evaluated and clear. Including previous works, breaking the method to parts etc.", "theoretical_claims": "It is unclear from the definitions pre l76 what exactly is Y, it seems to be the score (so \\in [0,1] given by some metric?) for a specific example (I assume the fact you kept using the word prompt aided the confusion). Is this argmax well defined? what is a random variable or sample in any of that? Or what is the model under which this is a probability (under a discreet set of values it is just 0/1)?\nYou write it in a very formal way, so one expects it is true formally, you can also explain it in a more intuitive manner or explain what your assumptions are here (probability because of uncertainty? Baseian thing with priors?) Or move things up (in l97 you define more).", "experimental_designs_or_analyses": "I would add something about what overheads your method adds. E.g. how much time it adds to each computation, how hard is it to implement (do you share a relevant implementation?), what other limitations may make someone not use this, as it seems really promising and the improvements are large as well.", "relation_to_broader_scientific_literature": "You can cited something in l46, no need to claim it as if it was a new claim (e.g. a survey or a tutorial on efficient benchmarking or just the efficient benchmarking paper for NLP).\n\nThe distinction between active evaluation and active testing is that all active testing reduces the amound of scoring function uses and you reduce the amount of inference uses? I would make this distinction clearly (especially as you bring a new very similar name and only discuss active testing in the related work. Also, why do you add \"acquisition\"? Seems to imply another difference but I don't think there is. Moreover, if you claim a new approach, why not own it? Describe the difference in general and the new name and distinction and then offer your specific method.)", "essential_references_not_discussed": "Each time I thought something was missing I instead found I was hasty to judge and it appeared somewhere else in the paper, very well documented.", "other_strengths_and_weaknesses": "The main prospect the work claims to deal with is the wrong one. The paper doesn't do anything with prompts. A certain example (abstract task, output pair) can have multiple prompts (e.g. \"what is the capital of France?\" or \"What is the capital of france\" or even \"hey, dude, do you know what's the capital of France\"). There is a lot of work on those things because this is an important issue in benchmarking LLMs, it took me several pages before I figured what this paper is trying to do.\nAn example to give the difference, you mention Polo's early work often (tinybenchmarks) but not their *prompt* related work and the name tells all the difference (Efficient multi-prompt evaluation of LLMs). It sounds like this paper should be compared to it, but in fact it is not.", "other_comments_or_suggestions": "l 207(right) $h$ probably a typo", "questions_for_authors": "Why do you call your method \"RL\" in the graphs? Rather than the name of your method. Also your variants I would add in the legend (ours) or some other way to separate those from previous work.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper studies efficient evaluation of large language models, and proposes a novel RL-based policy for selecting a subset of test examples by leveraging captured dependencies across them. Empirical results demonstrate that the proposed approach can substantially reduce the number of test examples needed while maintaining accurate performance estimation. Reviewers unanimously recommend acceptance.
Decision: Accept (poster)