On the Worst Prompt Performance of Large Language Models

Authors: bowen  cao
deng  cai
zhisong  zhang
yuexian  zou
wai  lam
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-15 09:15:46

Abstract: The performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts, which raises significant concerns about their reliability in real-world scenarios. Existing studies often divide prompts into task-level instructions and case-level inputs and primarily focus on evaluating and improving robustness against variations in tasks-level instructions. However, this setup fails to fully address the diversity of real-world user queries and assumes the existence of task-specific datasets. To address these limitations, we introduce RobustAlpacaEval, a new benchmark that consists of semantically equivalent case-level queries and emphasizes the importance of using the worst prompt performance to gauge the lower bound of model performance. Extensive experiments on RobustAlpacaEval with ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families uncover substantial variability in model performance; for instance, a difference of 45.48% between the worst and best performance for the Llama-2-70B-chat model, with its worst performance dipping as low as 9.38%. We further illustrate the difficulty in identifying the worst prompt from both model-agnostic and model-dependent perspectives, emphasizing the absence of a shortcut to characterize the worst prompt. We also attempt to enhance the worst prompt performance using existing prompt engineering and prompt consistency methods, but find that their impact is limited. These findings underscore the need to create more resilient LLMs that can maintain high performance across diverse prompts.

Main paper: 
 introduction: 
 In recent years, large language models (LLMs) have made extraordinary progress (Brown et al., 2020; Touvron et al., 2023; Jiang et al., 2023; Team et al., 2024) and one key factor in their success is their ability to adapt to diverse tasks through prompting. However, the performance of these LLMs is significantly sensitive to the prompts they receive (Gu et al., 2022; Mizrahi et al., 2024; Sun et al., 2023; Li et al., 2024) . Even minor alterations in format, without semantic changes, can trigger substantial performance degradation (Sclar et al., 2023) . Consequently, prompt engineering has surfaced as a critical component, playing a critical role in unlocking the full potential of these models (Shin et al., 2020; Zhou et al., 2022a; Pryzant et al., 2023; Prasad et al., 2023; Gonen et al., 2022; Schulhoff et al., 2024) . Figure 1 : An example illustrating the gap between existing benchmarks that evaluate prompt consistency and real user queries.Despite the efficacy of prompt engineering, it is not without its drawbacks. First, it is unrealistic to expect users to master the art of designing optimal prompts or to invest a significant amount of time in doing so. Second, automatic prompt engineering often necessitates testing on substantial labeled data to pinpoint the most effective prompts (Shi et al., 2022; Prasad et al., 2023) , a process that is impractical for users who just have one or a few unlabeled queries. Therefore, investigating LLMs' robustness to prompt variances is of great research and practical value.Previous research on prompt robustness (Mizrahi et al., 2024; Sclar et al., 2023; Sun et al., 2023) divides a user query into two parts: task-level instruction and case-level input. The task-level instruction is an abstract task definition and explanation supplemented by case-level concrete input (See the left part of Figure 1 ). This setup is reminiscent of conventional NLP practice, where taskspecific models are developed using a testing set for each well-defined NLP task. Consequently, they have focused on the LLM's resilience to task-level instructions exclusively, reporting the average performance across testing set cases. Nonetheless, these studies have limitations. First, they disregard that the best task-level instruction might vary across individual cases. Second, they overlook the impact of variations in case-level input on model performance. Last but not least, real-world user queries often do not explicitly segregate task-level instruction and case-level input (See the right part of Figure 1 ). These queries may cover a wide array of tasks and it is not possible to optimize the prompts through evaluating on a task-specific testing set.In this paper, we present a comprehensive study that goes beyond the conventional approach of evaluating LLMs. We shift the focus from task-level instructions to diverse real-world user queries. Our work introduces a new benchmark, ROBUSTALPACAEVAL, that includes semantically equivalent case-level queries across various tasks, offering a more holistic analysis. We argue that the worst prompt performance, defined as the lowest performance a model exhibits across different paraphrases of a query with equal semantics and fluency, is a crucial metric for assessing the lower bound of LLM performance. Our extensive experiments on ChatGPT (gpt-3.5-turbo-1106) and six opensource LLMs from the Llama, Mistral, and Gemma families reveal substantial variability in model performance. For instance, Llama-2-70B-chat model shows a difference of up to 45.48 points in winrate against GPT4 using RobustAlpacaEval, and its worst prompt performance can be as low as 9.38%. Our findings further reveal that the worst prompts, which cause the model to perform the worst in specific cases, are not universally applicable across different models. Each model also exhibits unique preferences towards all paraphrases, as demonstrated by their inconsistent performance rankings on identical cases. Beyond the absence of model-agnostic traits in identifying the worst prompts, model-dependent prompt features, such as perplexity and hidden states, also prove inadequate in forecasting model performance. Furthermore, we also find that different models are more sensitive in different cases, which further underscores the complexity involved in tackling the issue of worst prompt performance.In summary, the contributions of this paper can be summarized as follows:• We introduce a new benchmark, ROBUSTALPACAEVAL, that encompasses semantically equivalent queries of diverse real-world tasks, offering a more holistic assessment compared to existing benchmarks that focus solely on rephrasing task-level instructions.• Through extensive experiments on ChatGPT and six open-source LLMs from the Llama, Mistral, and Gemma families, we unveil significant variability in model performance and highlight the difficulty in predicting prompts that lead to the worst performance.• We show the shortcomings of most existing methods in improving the worst prompt performance of models. Yet a voting-based method effectively enhances the stability of model performance. 
related work: 
 Existing research on prompt robustness can be classified into two categories. On the one hand, research efforts have been focused on enhancing the inherent resilience of LLMs to prompt variations, namely prompt consistency. On the other hand, progress has been made in automating prompt engineering, the process to find the optimal prompt that yields the best performance.Prompt Consistency. Previous studies (Gu et al., 2022; Wang et al., 2023a; Zhu et al., 2023; Wang et al., 2023b; Salinas and Morstatter, 2024) have explored the robustness of LLMs to intentional perturbations such as word deletion and sentence shuffling. Our work deviates from these perturbation-based studies, as we are interested in model performance across semantically equivalent and syntactically fluent prompts. While it is anticipated that a flawed prompt would lead to performance decline, we do not expect such performance variations in our setting.Recent research (Mizrahi et al., 2024; Sclar et al., 2023; Sun et al., 2023) has also examined the variability in model performance with semantically equivalent prompts. However, these studies solely focused on task-level instructions and overlooked the variations within case-level inputs. Moreover, the models used in these studies are trained and evaluated on traditional NLP datasets, while today's LLMs are predominantly instruction-tuned on and serve diverse user prompts. This discrepancy suggests that the findings of these studies may not fully apply to the latest generation of LLMs.Prompt Engineering. A number of studies utilize gradient-based methods for prompt optimization (Shin et al., 2020; Shi et al., 2022; Li and Liang, 2021; Qin and Eisner, 2021; Lester et al., 2021; Liu et al., 2023) . However, their dependence on substantial labeled training data and gradient computing limits their applicability for users with only a few unlabeled queries or gradient-less APIs. Furthermore, these methods incur significant computational costs as the model scale increases.Conversely, gradient-free methods aim to find the optimal prompt through exploration and scoring (Prasad et al., 2023; Zhou et al., 2022a; Pryzant et al., 2023; Chen et al., 2023; Yang et al., 2023) . During the exploration phase, they create analogous prompts using techniques such as rephrasing the current prompts with an LLM. Subsequently, the best prompts are selected based on the model's performance on downstream tasks. This process can be iterative. Although these approaches can proficiently pinpoint prompts that result in better performance for a specific task, they all necessitate a test set for prompt scoring. However, in real-world scenarios, such test sets are often absent. Gonen et al. (2022) suggests that prompt perplexity can be an effective data-free criterion. However, our experiment results tell a different story, indicating that prompt perplexity is not a reliable metric.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes] Justification: In Section 6 (Conclusion).Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
benchmarking the worst prompt performance: 
 We present the construction process of our new benchmark, ROBUSTALPACAEVAL ( §3.1) and report the results on ChatGPT and six open-source LLMs from Llama, Mistral, and Gemma families.3.1 A New Benchmark: ROBUSTALPACAEVAL Data. Our benchmark is based on TinyAlpaceEval (Polo et al., 2024) , which is a condensed subset of the AlpacaEval (Li et al., 2023) benchmark, created to enable efficient assessment of LLMs. We develop ROBUSTALPACAEVAL by creating ten 4 paraphrases for each query within TinyAlpacaEval. To save manual efforts, this is first accomplished automatically through GPT4. Subsequently, each paraphrase is manually reviewed and revised to ensure semantic integrity and human-like fluency. We Table 1 : Results on our ROBUSTALPACAEVAL benchmark. The model order is arranged according to their original performance. The substantial range between the worst and best performance suggests the robustness issues in LLMs' instruction-following ability. Scaling up model sizes, while improving average performance, does not enhance robustness. use carefully crafted prompts to promote the diversity of paraphrases, which is evidenced by the fact that the average of length-normalized edit distance5 between each pair of paraphrases is 0.7234 at the word level. The few-shot paraphrasing instruction utilized in this process is detailed in Appendix A.2.In line with common practice, we use weighted win-rate (Li et al., 2023) as our performance metric; It uses an evaluator to compare the output of the target model against that of a reference model, and estimates the winning probability of the target model. Specifically, we employ the gpt4_turbo model as the evaluator and the reference model. We term the model's performance on the original prompt as original performance. We also report the worst, best, average performances across all eleven prompts as well as the standard deviation. For each metric, we average the results across all cases in ROBUSTALPACAEVAL.The results shown in Table 1 There is a significant gap between the worst performance (lower bound) and best performance (upper bound) for all models. For instance, the worst and best performance of Llama-2-70B-chat are 0.094 and 0.549, respectively, indicating a difference of 0.455. This suggests that the current LLMs' ability to follow instructions is not robust enough. Even instructions with identical semantics and fluent expressions could lead models like Llama2-70B-chat to plummet from a level comparable to GPT4 (0.5 indicates equivalence to the reference model) to far below the average level (0.292).While scaling up models enhances their ability to follow instructions, it does not correspondingly increase their robustness. For example, the average performance of Llama-2-7B/13B/70B-chat shows a marked improvement, rising from 0.195 to 0.24, and finally to 0.292, but their robustness (indicated by the Standard Dev.) exhibits a slight decline, recorded at 0.133, 0.163, and 0.156 respectively. A similar trend can be observed with the Gemma family models. Despite Gemma-7b outperforming the 2b model with an average performance of 0.31 compared to 0.153, its robustness is inferior, registering at 0.191 compared to the 2b model's 0.118.The original performance assessment only provides a narrow perspective of a model's overall performance. Specifically, the original performance serves as a reliable metric for ranking within the same model family, as it aligns well with the best, worst, and average performances. However, we find that this alignment weakly correlates when comparing across different model families, such as Gemma and Llama, questioning the methodological validity of using just one type of phrasing for a query to assess performance. Furthermore, the original performance does not fully encapsulate a model's potential, including the boundary and average of its performance, and fails to reflect the stability of the model's performance.Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?Answer: [NA] Answer: [No] Justification: Upon acceptance, we will make our code publicly accessible. Additionally, we will ensure to provide comprehensive details to facilitate the complete reproduction of our results.Guidelines:• The answer NA means that paper does not include experiments requiring code. Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
identifying the worst prompts: 
 Given the noticeable performance disparities across semantically equivalent prompts, our next question is: can we identify the worst prompt among these paraphrases? This would not only aid our understanding of the model's lower bound but also be instrumental in improving model performance by guiding the refinement of prompts. We investigate this matter from both model-agnostic ( §4.1) and model-dependent ( §4.2) perspectives.We examine the model-agnostic attributes of the worst prompts from two perspectives: (i) we assess whether the worst prompts overlap across diverse models, and (ii) we probe whether the rankings of prompts are consistent across different models. Overlap of the worst prompts across different models. If many of the worst prompts are modelagnostic, there must exist certain prompts that, among their semantically equivalent paraphrases, consistently rank as the worst performing across all models. To quantify the prevalence of such prompts, we calculate the rate of the model-agnostic worst-k prompt. Let W m (x, k) denote the worst-kperforming prompts for model m on case x, and let the set of worst-k prompts for model m on datasetD be W m (D, k) = x∈D W m (x, k).We calculate the overlap rate of the worst-k prompts for the tested models, which ranges from 0 to 1, as follows:R(M, k, D) = m∈M W m (D, k) k * |D| (1)As shown in Figure 2 , the overlap rate of the worst-k prompts for Llama/Gemma family models is noticeably higher than that for all models, indicating a stronger consistency among models from the same family. However, even so, when k = 1 (considering only the worst prompt), the metric for Llama family models is only 2% (13% for Gemma), and less than 10% (20% for Gemma) when k = 2. Not to mention the results of all models are much lower, nearly zero. These results suggest that the prompts showcasing the lower-bound performance of the models are often model-specific.Performance Rankings of prompts across different models. We continue to check if the rankings among different models are consistent. We utilize Kendall's W (Kendall and Smith, 1939) , commonly referred to as the coefficient of concordance, as a means to quantify the correlation among performance rankings derived from various models. Mathematically, Kendall's W is defined as follows:W = 12 n i=1 (R i -R) 2 k 2 (n 3 -n) (2)where R i represents the sum of the ranks assigned to the i-th prompt by all models. R is the mean of total ranks, calculated asR = 1 n n i=1 R i .k is the number of models, and n is the number of prompts being ranked. The value of W ranges from 0 to 1, where 0 indicates no agreement and 1 indicates complete agreement among the judges6 . We compute the average Kendall's W across all cases as the overall consistency. The results presented in Table 2 reveal the following observations: (i) The rankings within the Llama and Gemma family models exhibit only moderate consistency. The slightly higher consistency of the Gemma family models could be attributed to the fewer scales (2b/7b). This suggests that individual models within the same family still possess their unique strengths and weaknesses. (ii) The consistency between all models is significantly lower, underscoring the difficulty in establishing a model-agnostic standard for determining "good" and "bad" prompts. Table 2: We report the average value of Kendall's W across all cases. We also calculate the proportion of cases with different levels of consistency. The ranges for each level of consistency are as follows: Negligible (W < 0.1), Weak (0.1 ≤ W < 0.3), Moderate (0.3 ≤ W < 0.5), and Strong (W ≥ 0.5). Overlap of Sensitive Cases. The above experiment results underscore that the worst prompt is almost unpredictable in advance without given the model. Our next question is whether different models suffer from the same prompt variances. Concretely, we classify a case as a sensitive case if and only if the model's performance range (the difference between the best and worst performances) exceeds a threshold (e.g., 0.5). 7Figure 10 in Appendix B presents an example of a sensitive case. We measure the overlap of sensitive cases between different models by calculating the average Intersection over Union (IoU) of each pair of models within the tested model set. Let S m (D) represent the set of sensitive cases for model m on dataset D. The overlap measure of the tested model set M is:EQUATIONEQUATIONFigure 3 illustrates the fluctuation of IoU in response to varying sensitive case thresholds across diverse model sets. The observations are as follows: (i) The IoU is markedly higher among the Llama/Gemma family models, indicating a greater commonality of vulnerabilities within the same model family. For models from different families, such as the 7B models of Llama, Gemma, and Mistral, their IoU is lower despite similar overall performance (as shown in Table 1 ). (ii) When considering all models, the IoU drops significantly to less than 0.2, suggesting the absence of a clear model-agnostic characteristic. (iii) As the threshold increases, there is a consistent decrease in the IoU across all model sets. This suggests that the most sensitive cases for a model are often unique to it, thereby emphasizing the existence of distinct issues inherent to each model.The above experiments demonstrate that the performance rankings of different prompts are inconsistent among different models. Furthermore, different models may suffer from different prompt variances. Therefore, it is unlikely to characterize the worst prompts using modelindependent features. To this end, we omit further analyses on the relationship between model performance and linguistic features of the prompts, such as sentence length, syntactical complexity, wording choice, and paraphrasing methods.The findings from §4.1 suggest that we can hardly predict the prompt performance in advance without access to the model. We then turn to explore the possibilities of building the worst prompt predictors using model-aware features. • Min-K% Prob. We also explore the potential relationship between model performance and the prompt's Min-K% Prob (Shi et al., 2023) , a method related to pre-training data detection, which judges the model's familiarity with the text based on the average log-likelihood of the lowest k% tokens in the sentence. Intrigued by this, we carry out an analysis akin to that for perplexity, only to find weak positive correlations that lack statistical significance, as shown in the right half of Figure 4 .• Hidden States. We shift our focus to investigate model perceptions of prompt quality through the lens of model representation, using: (i) Principal Component Analysis (PCA) for direct visualization of hidden states, as shown in Figure 5 (a). The color gradient reflects the model's performance ranking across 11 prompts per case, revealing no distinct separation between different performance levels. (ii) Probing technique to quantify the extent to which the model's internal representations are predictive of its performance. We train a reward model (a 3-layer MLP in practice) that scores prompts based on hidden states and apply the standard training loss function (Ouyang et al., 2022) , which is defined as:EQUATIONwhere P represents the entire set of paraphrase pairs, each pair being formed by combining two paraphrases from the same case, r θ (p) is the output of the reward model, and p g is the preferred prompt when compared to p b in a pair. We implement two training-test set partitioning strategies: (i) Intra: Dividing prompts within each case into training and testing sets at a 3:1 ratio. (ii) Inter: Dividing all cases into training and testing sets at a 3:1 ratio. For each case in the test set, we pair up the paraphrases and determine the correctness of the model's scoring on each pair based on their performance ranking. We report the average correctness rate across all pairs as the accuracy. As shown in Figure 5 (b), the accuracy is near chance (0.5), suggesting hidden states provide limited insight into prompt quality.• Model Preference. We then delve into the model's ability to perceive the quality of prompts.We begin by giving the model two prompts and asking it to choose the one that would enable it to generate a more helpful, accurate, and comprehensive response. In all experiments, we test both arrangements of the given pair of prompts to eliminate any bias that may arise from their positioning. The metric we report is the proportion of times the model selects the prompt that leads to its superior performance. As shown in Table 3 , given all pairs of paraphrases, the accuracy of the Llama family models and ChatGPT hovers around 50%, with Llama-2-70B-chat slightly higher at 53.02%.When dealing with worst-best pairs, although the performance of all models, except for Llama-7B-chat, slightly improved, the highest score is only 58.92, suggesting that the models are largely unable to perceive the impact of the given prompts on their own performance.Discussion Our explorations over prompt perplexity, Min-k% Prob, hidden states, and model preference show that it is very challenging to identify the worst prompt in advance even with the access to the model. Note that this difficulty is often overlooked in previous studies as they primarily focus on task-level instructions and assume a corresponding test set for prompt selection. Specifically, they obtain the generation results on the test set and directly measure the downstream task performance. However, this is not possible for diverse real-world user queries, which further underscores the importance of the case-level setup in ROBUSTALPACAEVAL. 
improving worst prompt performance: 
 As discussed in §2, prompt engineering and prompt consistency approaches are commonly employed to improve model performance on arbitrary prompts. In this section, we investigate the effectiveness of applying these techniques to improve the worst prompt performance.Prompt Engineering Most existing prompt engineering methodologies fall short in tackling the worst-performance issue, due to the lack of a test set for a single unlabeled prompt. Therefore, we explore two alternative strategies: (i) allowing the model to refine the prompts itself, and (ii) implementing a voting-based generation process that takes into account all paraphrases.• Self-refinement. First, we allow the model to rewrite the given prompts according to its own preferences, making them more conducive to generating better responses.• Voting. Another solution is to let the model perform voting-based generation based on all prompts, which can be mathematically represented as:P (y i |X, y <i ) = E x∈X P (y i |x, y <i ) (6)where X represents the set of paraphrases. Prompt Consistency Drawing inspiration from the swarm distillation method proposed by Zhou et al. (2022b) , we implement an unsupervised consistency regularization strategy that encourages the model's predictions for various paraphrases to converge.• Swarm Distillation. To construct the training data, we utilize the SFT set (10k prompts) from the alpaca-farm dataset, and collect paraphrases based on the strategy outlined in Section 3.1. For all prompts in any given case, we sample x i and x j from them and guide the output of x j based on the model's output for x i , thereby training the model in an unsupervised manner. To devise an unsupervised model selection criterion, we define the following metric:EQUATIONwhere Sij is the mean of S ij over j. The value of C(X, Y ) ranges from 0 to +∞, reflecting the degree of consistency in the model's log confidence of generating outputs (Y ) based on given instructions (X), with 0 indicating totally consistent. We obverse that the consistency always decreases at first, followed by increasing, which aligns with the findings in Zhou et al. (2022b) . We train models based on LoRA and stop training at the checkpoint from which C(X, Y ) starts to increase.Results Table 4 presents several key findings: (i) Model performance significantly declines when subjected to self-refined prompts, as demonstrated by Llama-2-7/13/70B-chat model's average performance, which dropped by 10.04%, 13.15%, and 13.53%, respectively. This trend suggests that direct prompt engineering without reference to test data can lead to degraded model behaviors.(ii) When applying the voting method, the model's best performance significantly decreases, and the average performance may also be affected (a 6.71% drop for Llama-2-13b-chat). However, it effectively improves the worst performance, for instance, boosting the Llama-2-70B-chat model by 21.98, surpassing its average performance. We believe this method is particularly effective in scenarios where the lower bound of model performance is of concern. This is because, even when faced with a less-than-optimal prompt from the user, the paraphrasing-then-voting strategy can still deliver satisfactory results. However, this improvement comes at the cost of a several-fold increase in computational expense. (iii) While swarm distillation enhances the model's performance consistency, it unfortunately results in a decrease in overall performance. This is likely because the model is over-fitting to its self-generated outputs, which could introduce noise or be of lower quality than its original training data, thereby reducing the overall performance of the model. 
conclusion: 
 In conclusion, this paper addressed a critical gap in understanding the robustness of LLMs to prompt variations. We introduced a new benchmark that shifts the focus from task-level instructions to caselevel queries. Extensive experiments on ChatGPT and six open-source LLMs, revealed the substantial performance variability across different prompts, the challenge of predicting worst prompts, and the limited efficacy of existing methods for improving the worst prompts performance. Our findings underscored the importance of continued research into prompt robustness in more realistic settings.Despite the efforts, our study is not without limitations. The range of models we examined may not be extensive enough to capture a full spectrum of insights. Additionally, alternative approaches to pinpointing worst prompts could offer deeper understanding. We advocate for future studies to delve into these areas, enhancing our collective understanding. Table 5 : The average score of the Llama-2-70B-chat model across all possible combinations of different numbers of paraphrases. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? Answer: [No] Justification: The primary focus of this paper is the development of a novel benchmark and the preliminary assessment of model performance. The benchmark has been curated to assess the models' worst prompt performance, rendering the inclusion of error bars less pertinent to our primary research goals. Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).• The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. • It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. • For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). • If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text. 
experiments compute resources: 
 Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?Answer: [No] Justification: Given that our experiments do not require substantial computational resources, we have not specifically outlined the computer resources needed for the experiments.Guidelines:• The answer NA means that the paper does not include experiments.• The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage. • The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. • The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). 
code of ethics: 
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broader impacts: 
 Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [NA] Justification: This paper focuses on the prompt robustness of large language models and does not introduce privacy, security, or fairness issues.Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.• Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [NA] Justification: The paper does not present any such risks. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: In Section 3 (Benchmarking the Worst Prompt Performance). Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. 
 
 Appendix: Basically, the worst performance decreases and the best performance increases monotonically with the number of paraphrases. While it is impractical to list all possible paraphrases and calculate the exact values, we find that these scores converge quickly with a sufficiently large number of paraphrases. Taking Llama-2-70B as an example, we calculate the worst and best performances, along with their difference, for each combination of n (ranging from 1 to 11) paraphrases of a case. Then, we report the average of these scores across all combinations. The results are presented in Table 5 . As the results show, with the increase in the number of paraphrases, the average of the model's best/worst performance initially changes significantly and then levels off. For other models, we observe the same trend of changes. To balance the evaluation efficiency and robustness, we decided to construct 10 paraphrases for each query.The few-shot instruction we used for paraphrasing is as follows:Your task is to generate 10 unique paraphrases for the given query, ensuring that the meaning of each paraphrase remains consistent, and the structure is significantly altered. Do not introduce any new information that isn't present in the original query, and avoid omitting any crucial information from the original query, particularly any specific requirements about the output content, style, format, options, or any numbers or data. The responses to your paraphrases and the original query should be identical. If the original query contains quoted or referenced content, such as 'rewrite the following sentence: <reference>' and those enclosed in '<br><br>', you should retain this referenced content in your paraphrases and rephrase the rest of the text. Your task is not to answer the query, but solely to rephrase it. Please provide unique and creative rephrasing. Please note that you should provide exactly 10 paraphrases, not less. Output each paraphrase separated by "||||". The examples provided below are for illustrative purposes only. In the examples, only one paraphrase is provided and the other nine are omitted for brevity. However, in your output, you should provide all 10 paraphrases.Query: "Why is it that only proteins, carbohydrates and fats are deemed to have caloric value?<br>Just to clarify -consider the hypothetical situation below:<br>100g of 'carbohydrates' = 400kCal<br>100g of 'protein' = 400kCal<br>100g of 'fat' = 900kCal<br>100g of 'vitamins, minerals and/or other electrolytes' = ?" Output: Why are proteins, carbohydrates, and fats the only substances considered to have caloric value? To provide further clarification, let's consider the hypothetical scenario below: If 100g of carbohydrates provide 400kCal, 100g of protein provides 400kCal, and 100g of fat provides 900kCal, what would be the caloric value of 100g of vitamins, minerals, and/or other electrolytes? |||| <other nine paraphrases are omitted for brevity> Query: "I have this text: <br><br>We use professional processes. Use the photo delivery system, check the quality and send it to the customer immediately. We started editing digital photos from the first wave of 2000. Started with editing personal photos, and then spread our passion by editing wedding photos or restoring old photos for loved ones, friends and family. friends and cus-tomers. 2005 started a local photo shop and started commercial photo editing, product photography for manufacturers. 2007 implemented online projects on freelancer platforms.<br><br>Rewrite it in professional style to tell a touching story." Output: I possess this text: <br><br>We use professional processes. Use the photo delivery system, check the quality and send it to the customer immediately. We started editing digital photos from the first wave of 2000. Started with editing personal photos, and then spread our passion by editing wedding photos or restoring old photos for loved ones, friends and family. friends and customers. 2005 started a local photo shop and started commercial photo editing, product photography for manufacturers. 2007 implemented online projects on freelancer platforms.<br><br>Rephrase it in a professional manner to narrate a moving story.Query: "I want you to act as a Tiktok Ads Keywords generator. Your task is to come up with keywords that can be used in Tiktok advertising campaigns. The keywords should be relevant to the target audience and the product or service being advertised. Your responses should not include more than 5 keywords per request." Output: Your role will be to serve as a TikTok Ads Keywords generator. Your objective is to generate relevant keywords that can be utilized in TikTok advertising campaigns. These keywords should effectively target the desired audience and align with the product or service being promoted. Please provide no more than five keywords per request in your responses. Now, please generate 10 paraphrases for the given query.Query: {input_query} Output: Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.We find that 10 paraphrases are sufficient to steadily reflect the variability in the model's performance. Further details can be found in Appendix A.1.We compute the edit distance between prompts x and y normalized by their average length as: 2 * EditDistance(x, y)/(|x| + |y|)W values less than 0.1 are often considered as no or negligible agreement; 0.1 to 0.3 suggest weak agreement; 0.3 to 0.5 indicate moderate agreement; and W values above 0.5 show strong agreement(Sidney, 1957).We also attempt to define the sensitivity through standard deviation and the coefficient of variation, finding results very similar to those obtained when using range as the metric. 


Decision: The paper studies the worst performance of LMMs through a new benchmark they propose as part of this study that is composed of semantically equivalent queries generated and proposes to use the lower bound of model performance that helps characterize a lower bound for model performance. The reviewers unanimously recommend its acceptance. They mention that the paper is well written, noted the importance of the problem and commended on the thoroughness of experiments. I therefore recommend its acceptance.

Reviews: review: paper_summary: paper_summary: This paper studies the worst performance an LLM can have on input queries by testing on paraphrases of each query and report the original, worst, best, and average performance. It has been found that there is a large gap between the best and worst performance. It is then found that there is no particular "worst prompt" among all models or even models from the same family. Furthermore, model-dependant features like are also not useful in identifying the worst prompt. Finally, authors explored a few approaches to try to improve the "worst performance". In particular, voting seems effective in improving the lower bound at the cost of harming the upper bound.
strength_weakness: strength_weakness: - I think the definition of the "worst" or "best" performance is not clearly stated (line 121). If I understood correctly, the worst performance will monotonically drop if we have more paraphrases of each query. This is linked to the question below and I hope that the authors can clarify on this.
- Using paraphrases to study prompt variation could introduce errors and paraphraser/LLM preference mismatch. It might be questioned that Table 1 and Table 4's average performance is almost always lower than the original performance. Does this mean that the paraphrasing process has introduced errors, domain shifts, etc?
questions: questions: - I wonder if you could provide a clear and reproducible definition of the worst and best performance in mentioned line 121. From the writing, I think regarding whether a query is correctly responded by the model: worst=all(prompt1, prompt2, ...) and best=any(prompt1, prompt2, ...). Is this correct? 
    - If this is the case, I think the more paraphrases we have, the higher we get for the best performance and vice versa for the worst performance. This might make the number of paraphrases a bit arbitrary. The worst-best gap might also be inflated.


- Throughout the paper, starting with line 15, I think the phrase "worst prompt" is not precise---we cannot call them good or bad per se unless proven. I think that you mean the prompt paraphrase that leads to the worst performance.
limitations: limitations: The paper touches on limitations like not experimenting enough models and enough techniques in finding the "worst prompt".
paper_summary: paper_summary: This paper proposes a new benchmark, RobustAlpacaEval, a benchmark with semantically equivalent queries. The authors evaluate performance as the worst performance across all the prompts and show that many prompt consistency methods have a limited improvement on this benchmark. The paper also shows that it is difficult to predict which prompts will have a poor performance.
strength_weakness: strength_weakness: - The variance of a model to different prompts has been studied several times in the literature (for example, see [1]), and it is unclear to me how a benchmark measuring worst-case performance would be used. I would appreciate if the authors provided more clarity on how they envision that this work could shed insight and facilitate future efforts.
- In Section 5, only Llama models are considered, which makes it unclear whether the insights discovered also apply to other models or are unique to Llama.
- The paper would be much stronger if the authors proposed a method to mitigate the phenomenon demonstrated in the paper, or if the authors gave a detailed explanation of why the identified phenomenon occurs.

[1] https://arxiv.org/abs/2401.03729
questions: questions: - Can the authors suggest some potential directions for resolving the issue raised in the paper?
- When creating a benchmark like this, the number of prompts can be increased arbitrarily, and the accuracy could be extremely low if the right prompts are chosen. Similarly, one could create an adversarial setting to test the robustness of each model. Why did you design the benchmark in this way?
- How do the authors envision that this benchmark be used? For example, should all future models be forced to evaluate on RobustAlpacaEval? Because there are many more prompts for each sample, this would lead to a multiplicative factor on the cost for inference.
limitations: limitations: Yes
paper_summary: paper_summary: In this paper, the author(s) propose a benchmark for prompt performance of large language models. In particular, the author(s) leverage GPT4 to generate variants of prompts and hence constitute a dataset. Afterwards, the author(s) evaluate the performance of these generated prompts, and explore the identification and improvement of worst cases.
strength_weakness: strength_weakness: - The methodology and result analysis of this study can be elaborated.
- Result visualization can be improved for better readability.
questions: questions: - Section 2: A related work can be added and analysed. Schulhoff, S., Ilie, M., Balepur, N., Kahadze, K., Liu, A., Si, C., ... & Resnik, P. (2024). The Prompt Report: A Systematic Survey of Prompting Techniques. arXiv preprint arXiv:2406.06608.
- Please elaborate the motivation and result analysis for exploring the hidden states in Section 4.2.
- I wonder if the author(s) consider sharing the dataset used in this project?
- The author(s) can consider specifying the selection criteria of LLMs used in experiments.
- Please consider using bold in tables to highlight the noteworthy data.
- It will be interesting to summarize the features/patterns of worst prompts for different LLMs.
- Please adjust the place of tables and figures to improve readability. For instance, Figure 3 is on Page 6 while it is described on Page 7.
limitations: limitations: - The author(s) note that the research can be extensive by including more LLMs, and applying alternative approaches to identify worst prompts.
paper_summary: paper_summary: This paper introduces a new benchmark, ROBUSTALPACAEVAL, that contains semantically equivalent queries of diverse real-world tasks, and uses it to conduct extensive experiments on ChatGPT and six open-source LLMs. It highlights variability in model performance and difficulties in predicting the worst prompt.
strength_weakness: strength_weakness: - The methodology utilizes gpt4_turbo as the evaluator and the reference model for outputs, which might introduce biases. A discussion of this potential bias should be included.
- The ROBUSTALPACAEVAL benchmark contains 10 paraphrases per given query. It’s unclear how performance disparities between the worst and best prompts would change with an increased number of paraphrases. This might also affect the robustness of cases with significant performance gaps.
- While the study explores several LLM families including Llama, Gemma, and Mistral, it includes only one version of ChatGPT and does not specify which version. Whether the prompts are uniform across different ChatGPT versions is unknown.
questions: questions: - How is prompt perplexity calculated?
- Are there any common features among the worst-performing prompts within the same model family? The paper points out the difficulty in predicting the worst prompts, both model-independently and with model access. Given that the overlap rate of model-agnostic worst-4 prompts reaches about 40% in the Gemma family, an analysis comparing the features of the worst and best prompts could be insightful.
limitations: limitations: The paper includes a limited selection of LLMs, and the challenge of effectively improving the worst-performing prompts remains unresolved.
paper_summary: paper_summary: The authors propose a new benchmark to study the robustness of LLMs to prompt variations. Different from previous work, this paper mainly focuses on semantically equivalent prompts rather than taks-level instructions. The experiments demonstrate that many popular LLMs are sentitive to the form of prompts. Moreover, it is difficult to detect the worst prompt and improve the worst prompt performance.
strength_weakness: strength_weakness: 1. Lack of Solution: While the paper adeptly identifies the problem of prompt variability and its impact on LLM performance, it falls short of proposing an effective solution to mitigate this issue. The research would benefit from the inclusion of potential remedies or strategies to enhance prompt robustness.

2. Inconsistency in Description: There appears to be a discrepancy between the textual description and the visual representation (Figure 1) regarding the focus on task-level instructions versus case-level inputs. Clarification on this point is needed to avoid any confusion for the readers.

3. Limited Scope of Experimentation: The experiments, while extensive, do not include testing on more advanced models such as GPT4. The performance of the proposed benchmark on state-of-the-art models would provide a more comprehensive understanding of the robustness of the latest LLMs.

4. Language Specificity: The study seems to be limited to English language prompts. Given the global application of LLMs, it would be beneficial to extend the experiments to include other languages to assess the generalizability of the findings.

5. Prompt Diversity Validation: The paper does not explore whether incorporating a diverse set of prompts in the SFT stage could potentially improve model's worst prompt performance. An analysis of this nature would add depth to the paper's contribution.
questions: questions: See Weaknesses.
limitations: limitations: The authors have adequately addressed the limitations.

Comments: comment: Thank you for your valuable feedback and for firmly backing the acceptance of our paper. We are glad our rebuttal has addressed your concerns. We promise to incorporate all your suggestions and tackle any remaining issues in our updated paper. 

Thank you again for your insightful advice and continued support.
Thank you for your valuable feedback and the consideration of raising the score after our discussion. We are pleased that our rebuttal has addressed your concerns. We will also address these concerns and incorporate all your suggestions in our paper. 

Thanks again for your insightful comments that help improve our work greatly!
Thank you for your prompt reply. We fully agree with your view that "it is still far away from measuring robustness against real and adversarial attacks," and we would like to share some of our thoughts with you.

Firstly, while robustness against attacks is a critical security issue, our focus is more on model stability, particularly its response to prompts with varying but equally clear expressions of the same meaning. We observe that most users do not deliberately attack models, but their distinct language styles can unconsciously influence the quality of the model's outputs.

We believe that model stability is an area that deserves more attention, and the "worst prompt performance of models" we have proposed is an often-overlooked issue. Our work presents a dedicated and comprehensive analysis of measuring, predicting, and improving worst prompt performance. We hope our paper will inspire further research in this area.

Lastly, we appreciate your explicit statement of **“supporting the acceptance of the paper”**. However, a borderline accept score typically indicates uncertainty and a significant chance of rejection, in contrast to a weak accept or higher score, which signals a clear intent to publish. We kindly request that you consider adjusting the score upwards.

Thanks again for your insightful comments and encouraging reply.
The authors' response has addressed most of my concerns. I will raise my score to 6.
Thanks to the authors for the detailed response. The rebuttal has addressed most of my concerns. I will keep a positive score. Good luck!
Thanks to the authors for their response. The provided method certainly does measure some sort of robustness metric. In my humble opinion, this level of robustness is one step above just using one prompt, but is still far away from measuring robustness against real and adversarial attacks. 

In any case, I am still supporting the acceptance of the paper, and I will keep my score, which is above the acceptance threshold.
Thank you again for your positive feedback! We are grateful for your recognition of the value of the problem we study and the insights of our findings!

We hope our clarifications addressed your comments and we would like to inquire if you have any further questions or require additional information. Would you kindly be open to increasing your score? We are eager to provide any necessary support and continue the dialogue.
Thank you again for your positive feedback! We are grateful for your recognition of the value of the problem we study and the insights of our findings!

We hope our clarifications addressed your comments and we would like to inquire if you have any further questions or require additional information. Would you kindly be open to increasing your score? We are eager to provide any necessary support and continue the dialogue.
Thank you for your positive feedback! We are glad that our rebuttal addresses your concerns.  We will also address these concerns and incorporate all your suggestions in our paper. Thanks again for your insightful comments that help improve our work greatly.
Thank you again for your positive feedback! We are grateful for your recognition of the richness of our experiments and the contribution of our research to the community!

We hope our clarifications addressed your comments and we would like to inquire if you have any further questions or require additional information. Would you kindly be open to increasing your score? We are eager to provide any necessary support and continue the dialogue.
Thank you again for your positive feedback! We are grateful for your recognition of the value of the problem we study and the insights of our findings!

We hope our clarifications addressed your comments and we would like to inquire if you have any further questions or require additional information. Would you kindly be open to increasing your score? We are eager to provide any necessary support and continue the dialogue.
Thank you for the response. I think my questions are largely resolved. I have adjusted my score. Good luck. 

I agree with Reviewer H71g that a few other papers that have studied prompt variations and robustness can be referenced.