Title: On the Worst Prompt Performance of Large Language Models
Venue: NeurIPS 2024
Accepted: yes
Keywords: Natural Language Processing

Large Language Model

Instruction following

Model Robustness
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.

On the Worst Prompt Performance of Large Language Models ∗

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 modelagnostic 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. Data and code are available at https://github.com/bwcao/RobustAlpacaEval.

1 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).

Real-worldUserQueries

Assumeyou are a customer service representative.Please

Casel: As a customer service representative, kindly

Task-level

provide customer service to a customer regarding their

assist a customer who is facing challenges with our

Instruction

difficulties in accessing a website.

website, specifically reporting an inability to sign in

overthelastfour days.

Case1:The customer states that theyhave been unable to

Case2:Craft a narrative that brings to life a character

signinforthepastfour days.

sketched as a5-year-oldboywith aninsatiable

Case-level

Case2:Acustomer reports that they areunableto complete

curiosity and a penchant for questioning, while also

Inputs

an online purchase due tothe website's checkout process

harboring a strong aversion to adhering to rules. Feel

consistently failing.

freetoelaborateonhistraitsandweaveinincidents

thathighlighthispersonality.

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.

2 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.

3 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 distance 5 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.

Metrics. 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.

3.2 Results

The results shown in Table 1 reveal several key findings:

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.

4 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.

4.1 Model-agnostic Analysis

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 worstk prompt. Let W m ( x, k ) denote the worstk -performing prompts for model m on case x , and let the set of worstk prompts for model m on dataset D be W m ( D,k ) = ⋃ x ∈ D W m ( x, k ) . We calculate the overlap rate of the worstk prompts for the tested models, which ranges from 0 to 1, as follows:

0.4

overlaprateof worst-k prompts

Gemmafamily

一

Llama/Gemma/Mistral-7B

Allmodels

0.3

0.2

0.1

0.0

1

2

3

4

k

As shown in Figure 2, the overlap rate of the worstk 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:

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 as ¯ R = 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 judges 6 .

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.

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). 7 Figure 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:

0.6

Gemmafamily

0.5

Lama/Gemma/Mistral-7B

forsensitivecases

All models

0.4

0.3

loU

0.2

0.1

0.0

0.2

0.4

0.6

0.8

threshold

̸

Figure 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.

Discussion. 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.

4.2 Model-dependent Analysis

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.

70B

(0.60,1.00]

70B

(0.30,0.60]

(0.00,0.30]

13B

13B

(-0.30,0.00]

(-0.60, -0.30]

7B

(-1.00, -0.60]

7B

60.0%

40.0%

20.0%

0.0%

20.0%

40.0%

60.0%

60.0%

40.0%

20.0%

0.0%

20.0%

40.0%

60.0%

0

0.58

70B

60

1

2

0.56

0.54

13B

7B

3

40

4

0.52

5

0.50

6

0.48

20

7

8

0

25

50

75

100

125

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175

200

0

10

9

InterSplit

0.52

20

0.50

13B

70B

7B

40

0.48

75

-50

-25

0

25

50

75

0

25

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125

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200

Prompt Perplexity. Gonen et al. (2022) report that lower prompt perplexity generally correlates with superior model performance. To verify this assertion, we compute the Pearson correlation coefficients between perplexity and performance across all cases. The results are illustrated in Figure 4 (left half). The layout divides correlations into negative and positive (left/right of the 0 tick), with correlation strengths categorized as weak/no (0 to 0.3), moderate (0.3 to 0.6), and strong (0.6 to 1). Analyzing the Llama family models, we note the following: ( i ) About 45% of cases show weak or no correlation. ( ii ) Positive and negative correlations are nearly equally distributed. ( iii ) The distribution of cases across different correlation degrees is fairly balanced. These results are consistent with observations from other models analyzed (detailed in Appendix B), indicating no definitive correlation between prompt perplexity and model performance.

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:

where 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. Then, we evaluate the model in two ways: ( i ) by pairing all paraphrases and asking the model's preference, and ( ii ) by only inquiring about the model's preference between the best and worst prompts.

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%. Whendealing 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.

Table 3: In the model preference experiment, the model is tested with all paraphrase pairs (All Pairs) or best and worst prompt pairs (Worst-Best). We report the proportion of times the model prefers the prompt that leads to its better 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.

5 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:

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:

where ¯ S ij 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.

6 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.

References

Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. In Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, and Hsuan-Tien Lin, editors, Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , 2020.

Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. ArXiv preprint , abs/2307.09288, 2023.

Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. ArXiv preprint , abs/2310.06825, 2023.

Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, et al. Gemma: Open models based on gemini research and technology. ArXiv preprint , abs/2403.08295, 2024.

Jiasheng Gu, Hongyu Zhao, Hanzi Xu, Liangyu Nie, Hongyuan Mei, and Wenpeng Yin. Robustness of learning from task instructions. ArXiv preprint , abs/2212.03813, 2022.

Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, and Gabriel Stanovsky. State of what art? a call for multi-prompt llm evaluation. ArXiv preprint , abs/2401.00595, 2024.

Jiuding Sun, Chantal Shaib, and Byron C Wallace. Evaluating the zero-shot robustness of instructiontuned language models. ArXiv preprint , abs/2306.11270, 2023.

Siheng Li, Cheng Yang, Taiqiang Wu, Chufan Shi, Yuji Zhang, Xinyu Zhu, Zesen Cheng, Deng Cai, Mo Yu, Lemao Liu, Jie Zhou, Yujiu Yang, Ngai Wong, Xixin Wu, and Wai Lam. A survey on the honesty of large language models, 2024. URL https://arxiv.org/abs/2409.18786 .

Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. Quantifying language models' sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting. ArXiv preprint , abs/2310.11324, 2023.

Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) , pages 4222-4235, Online, 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-main.346.

Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. Large language models are human-level prompt engineers. ArXiv preprint , abs/2211.01910, 2022a.

Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. Automatic prompt optimization with" gradient descent" and beam search. ArXiv preprint , abs/2305.03495, 2023.

Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. GrIPS: Gradient-free, edit-based instruction search for prompting large language models. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics , pages 3845-3864, Dubrovnik, Croatia, 2023. Association for Computational Linguistics.

Hila Gonen, Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. Demystifying prompts in language models via perplexity estimation. ArXiv preprint , abs/2212.04037, 2022.

Sander Schulhoff, Michael Ilie, Nishant Balepur, Konstantine Kahadze, Amanda Liu, Chenglei Si, Yinheng Li, Aayush Gupta, HyoJung Han, Sevien Schulhoff, et al. The prompt report: A systematic survey of prompting techniques. arXiv preprint arXiv:2406.06608 , 2024.

Weijia Shi, Xiaochuang Han, Hila Gonen, Ari Holtzman, Yulia Tsvetkov, and Luke Zettlemoyer. Toward human readable prompt tuning: Kubrick's the shining is a good movie, and a good prompt too? ArXiv preprint , abs/2212.10539, 2022.

Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al. Decodingtrust: A comprehensive assessment of trustworthiness in gpt models. ArXiv preprint , abs/2306.11698, 2023a.

Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Neil Zhenqiang Gong, Yue Zhang, et al. Promptbench: Towards evaluating the robustness of large language models on adversarial prompts. ArXiv preprint , abs/2306.04528, 2023.

Jindong Wang, Xixu Hu, Wenxin Hou, Hao Chen, Runkai Zheng, Yidong Wang, Linyi Yang, Haojun Huang, Wei Ye, Xiubo Geng, et al. On the robustness of chatgpt: An adversarial and out-of-distribution perspective. ArXiv preprint , abs/2302.12095, 2023b.

Abel Salinas and Fred Morstatter. The butterfly effect of altering prompts: How small changes and jailbreaks affect large language model performance. arXiv preprint arXiv:2401.03729 , 2024.

Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) , pages 4582-4597, Online, 2021. Association for Computational Linguistics. doi: 10.18653/v1/ 2021.acl-long.353.

Guanghui Qin and Jason Eisner. Learning how to ask: Querying LMs with mixtures of soft prompts. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages 5203-5212, Online, 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.naacl-main.410.

Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages 3045-3059, Online and Punta Cana, Dominican Republic, 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.emnlp-main.243.

Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. Gpt understands, too. AI Open , 2023.

Yulin Chen, Ning Ding, Xiaobin Wang, Shengding Hu, Hai-Tao Zheng, Zhiyuan Liu, and Pengjun Xie. Exploring lottery prompts for pre-trained language models. ArXiv preprint , abs/2305.19500, 2023.

Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. Large language models as optimizers. arXiv preprint arXiv:2309.03409 , 2023.

Felipe Maia Polo, Lucas Weber, Leshem Choshen, Yuekai Sun, Gongjun Xu, and Mikhail Yurochkin. tinybenchmarks: evaluating llms with fewer examples. ArXiv preprint , abs/2402.14992, 2024.

Xuechen Li, Tianyi Zhang, Yann Dubois, Rohan Taori, Ishaan Gulrajani, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. Alpacaeval: An automatic evaluator of instruction-following models. https://github.com/tatsu-lab/alpaca_eval , 2023.

Maurice G Kendall and B Babington Smith. The problem of m rankings. The annals of mathematical statistics , 10(3):275-287, 1939.

Siegel Sidney. Nonparametric statistics for the behavioral sciences. The Journal of Nervous and Mental Disease , 125(3):497, 1957.

Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. Detecting pretraining data from large language models. ArXiv preprint , abs/2310.16789, 2023.

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems , 35:2773027744, 2022.

Chunting Zhou, Junxian He, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. Prompt consistency for zero-shot task generalization. In Findings of the Association for Computational Linguistics: EMNLP 2022 , pages 2613-2626, Abu Dhabi, United Arab Emirates, 2022b. Association for Computational Linguistics.

A Benchmark Construction

A.1 Determining the Number of Paraphrases

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.

A.2 Instruction for Paraphrasing

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.

Examples:

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:

B Identifying the Worst Prompts

mistral-7b

(0.60, 1.00]

mistral-7b

(0.30,0.60]

(0.00,0.30]

gemma-7b

gemma-7b

(-0.30, 0.00]

(-0.60, -0.30]

gemma-2b

(-1.00,-0.60]

gemma-2b

60.0%

40.0%

20.0%

0.0%

20.0%

40.0%

60.0%

60.0%

40.0%

20.0%

0.0%

20.0%

40.0%

60.0%

30

0

0

1

1

2

60

2

20

3

3

4

5

40

4

5

10

6

6

7

20

7

8

8

0

9

6

10

0

10

-10

-20

-20

-40

20

10

0

10

20

30

40

50

-40

-20

0

20

40

60

150

0

1

30

2

100

3

20

4

5

50

6

10

0

1

8

0

2

0

9

3

10

4

10

5

-50

6

-20

7

-100

8

9

-30

10

150

-100

-50

0

50

100

150

200

10

0

10

20

30

40

200

0

1

2

150

3

4

100

5

6

7

50

8

6

0

10

-50

-100

-200

-100

0

100

200

300

OriginalInstruction (Score:0.9922)

On thebasis ofthe subject of the email,determinewhether the email should betreated as spam or not.

Dermatologistsdon'tlikeher!

Output:

Based on the subject of the email, it is likely that the email should be treated as spam.The use of the phrase"Dermatologists don't like her" is likely to be a sensationalized or misleading claim ..

Paraphrase(Score:0.o005)

Output:

This email topic does not appear to be spam. Here's why: 1. Relevance: .. It is not a generic or random topic that is often associated with spam emails. 2. Urgency: ... 3. Personalization: The email is addressed to a specific person, which suggests that it may be a legitimate message rather than a mass email blast. 4. Grammar and spelling: The email appears to be well-written and free of obvious grammatical and spelling errors, which is another indicator that it may be a legitimate message.

Based on thesefactors,it is unlikely that this email should be categorized asspam.

NeurIPS Paper Checklist

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It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

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3. Theory Assumptions and Proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: The paper does not include theoretical results.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental Result Reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: In Section 3 (Benchmarking the Worst Prompt Performance), 4 (Identifying the Worst Prompts) and 5 (Improving Worst Prompt Performance).

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

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While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

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(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

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.

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The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

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At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental Setting/Details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: In Sections 3 (Benchmarking the Worst Prompt Performance), 4 (Identifying the Worst Prompts) and 5 (Improving Worst Prompt Performance).

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.

7. 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.

8. 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.

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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

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{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS:\n1. Research Value: The paper tackles a highly relevant problem that has substantial implications for the practical deployment of LLMs. The issue of prompt sensitivity is critical and the paper provides valuable insights into this domain.\n\n2. Clarity of Motivation: The authors have effectively articulated the motivation behind their work. The introduction and the problem statement are clear, setting the stage for the reader to understand the importance of the study.\n\nWEAKNESSES:\n1. 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.\n\n2. 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.\n\n3. 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.\n\n4. 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.\n\n5. 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.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nThe authors have adequately addressed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n- This work introduces a new benchmark focused on diverse real-world user queries rather than task-level instructions, which more closely mirrors real-world scenarios.\n- It conducts comprehensive evaluations across several LLMs and showcases variability in model performance across different prompts.\n- They tried several methods to improve the worst prompt and show the limitations of current method.\n\nWEAKNESSES:\n- 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.\n- 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.\n- 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.\n\nQUESTIONS:\n- How is prompt perplexity calculated?\n- 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.\n\nLIMITATIONS:\nThe paper includes a limited selection of LLMs, and the challenge of effectively improving the worst-performing prompts remains unresolved.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS:\n- This study acknowledges the variation and diversity of prompts in real-world, and adds value to the prompt engineering for LLMs.\n- The proposed benchmark is applied to multiple LLMs. Thorough comparison analysis is conducted.\n- The paper is well-organized, and the language is technical yet understandable for readers with domain knowledge.\n\nWEAKNESSES:\n- The methodology and result analysis of this study can be elaborated.\n- Result visualization can be improved for better readability.\n\nQUESTIONS:\n- 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.\n- Please elaborate the motivation and result analysis for exploring the hidden states in Section 4.2.\n- I wonder if the author(s) consider sharing the dataset used in this project?\n- The author(s) can consider specifying the selection criteria of LLMs used in experiments.\n- Please consider using bold in tables to highlight the noteworthy data.\n- It will be interesting to summarize the features/patterns of worst prompts for different LLMs.\n- 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.\n\nLIMITATIONS:\n- The author(s) note that the research can be extensive by including more LLMs, and applying alternative approaches to identify worst prompts.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n- The paper has a variety of key findings (Section 3.2). First, the authors find that there is a large gap between worst and best performance. Second, the authors find that scaling does not help robustness. Third, the authors find that there is little agreement between various models on which prompts are harder and/or easier. \n- The experiments are thorough and well conducted. \n- The findings open up doors for future research and efforts to make models more robust.\n\nWEAKNESSES:\n- 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.\n- 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.\n- 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.\n\n[1] https://arxiv.org/abs/2401.03729\n\nQUESTIONS:\n- Can the authors suggest some potential directions for resolving the issue raised in the paper?\n- 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?\n- 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS:\n- This paper discusses an important question to study, that is the lower bound performance of LLMs on different prompts. It is interesting to know that identifying the prompt that leads to bad performance is difficulty by looking at the prompts alone or using an LLM itself.\n\nWEAKNESSES:\n- 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.\n- 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?\n\nQUESTIONS:\n- 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? \n    - 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.\n\n\n- 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.\n\nLIMITATIONS:\nThe paper touches on limitations like not experimenting enough models and enough techniques in finding the \"worst prompt\".", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}