continue this discussion:

““The document explains a study that introduces a new method called SELF-INSTRUCT to improve how artificial intelligence language models (like GPT3) understand and follow human instructions. Unlike previous approaches, which depend on manually created instructions, SELF-INSTRUCT uses the language model itself to generate new instructions, inputs, and corresponding outputs. These self-generated instructions cover a wide range of tasks and are used to further train the model, making it better at following human commands.

The researchers start with a small set of manually written tasks (called seed tasks) and use these to prompt the language model to create new, diverse tasks. They carefully check the quality of these generated tasks, removing any that are too similar to existing ones or don’t make sense. This process results in a large dataset of unique and creative tasks.

Experiments showed that training GPT3 with this self-generated dataset significantly improved its ability to understand and execute new instructions it hadn’t seen before. This method even performed comparably to other models that had been trained on human-created instructions and annotations.

Furthermore, the researchers created and evaluated tasks that are more in line with real-world scenarios users might wish AI to assist with. These evaluations indicated that the model trained with SELF-INSTRUCT data performed better than models trained on publicly available datasets and was close in performance to other sophisticated models designed for instruction following.

Overall, the paper suggests that through SELF-INSTRUCT, it’s possible to significantly enhance a language model’s ability to understand and follow a wide range of instructions without needing extensive human annotation, thus opening doors to more adaptive and broadly capable AI models.””

The above is a summary of a paper entitled: “SELF-INSTRUCT: Aligning Language Models
with Self-Generated Instructions” Now, how does the following paper build upon this knowledge? Which method is more effective? How do the they differ?:

““
The Unreasonable Effectiveness of Eccentric Automatic Prompts
Rick Battle
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
VMware NLP Lab
Teja Gollapudi
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>
VMware NLP Lab

1.Introduction
In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) are playing a pivotal role in transforming the way humans interact with technology. As these models become increasingly sophisticated, understanding and influencing the nuances of their underlying functionality becomes imperative to harness their full potential. Among the myriad factors influencing the performance of language models, the concept of “positive thinking” has emerged as a fascinating and surprisingly influential dimension. Intuition tells us that, in the context of language model systems, like any other computer system, “positive thinking” should not affect performance, but empirical experience has demonstrated otherwise.
This paper aims to quantify the impact of various “positive thinking” additions to the system message of a prompt. In essence, it explores the influence of seemingly worthless prompt modifications by measuring the fluctuations in score for the outputs generated in response to multi-step reasoning questions from a benchmark dataset. As the quest for near-perfect performance from Artificial Intelligence (AI) intensifies, understanding the effect of “positive thinking” in language model prompts can add crucial performance points to test set scores. We will show that trivial variations in the prompt can have dramatic performance impacts. Then we’ll show that not only does systematic prompt optimization outperform “positive thinking”, even with smaller open-source models, but that it also generalizes better. Additionally, we’ll show that the highest-scoring automatically-generated prompt is remarkably different from anything a human practitioner would be likely to generate.
2.Related Work
The genesis of prompt engineering can be traced back to the seminal Chain of Thought paper by Wei et al. (Wei et al., 2023). This pioneering work demonstrated a significant enhancement in model performance by introducing a simple prompt modification: the inclusion of the directive “Think step by step.” The degree of performance improvement, however, is contingent upon the specific model, its size, and the underlying dataset.
Subsequently, the PaLM 2 Technical Report by Anil et al. (Anil et al., 2023) revealed that the application of Chain of Thought prompts may yield adverse effects on certain datasets. This observation underscores the absence of a universal prompt snippet capable of unconditionally improving model performance. Consequently, the landscape of prompt engineering has witnessed the emergence of resources such as the Prompt Engineering Guide1, aiming to catalog the myriad techniques and scholarly contributions2 constituting the expansive realm of prompt engineering. These endeavors reflect an ongoing effort to navigate the diverse techniques and insights propelling the continuous evolution of effective prompting strategies.
In addition to the formally published literature on prompt engineering, numerous discussions on less formal discoveries can be found in countless threads on social media platforms such as Twitter and Reddit3.
3.Experimental Design
To test the impact of “positive thinking” prompts, we vary the system message part of the prompt with a combination of “openers”, “task descriptions”, and “closers” in the following format:

{opener}{task_description}{closer}

Refer to Table 1, 2, and 3 for a comprehensive compilation of the opening snippets, task descriptions, and closing snippets utilized in our study. Given the incorporation of 5 openers, 3 task descriptions, and 4 closers, our experimentation involved a total of 60 unique combinations. Additionally, we conducted tests both with and without Chain of Thought prompting, resulting in a grand total of 120 prompt combinations per input per model. Although the possibility of expanding the range of snippets within each category existed (and the temptation to do so was strong), we made a deliberate decision to limit our selection due to the significant time commitment associated with the computational complexity of testing, as exemplified by the runtime required for 60 prompt combinations for a 70-billion-parameter model with Chain of Thought being measured in days, not hours.
Openers
None
You are as smart as ChatGPT.
You are highly intelligent.
You are an expert mathematician.
You are a professor of mathematics.
Table 1.Opening snippets for the system message.
Task Descriptions
None
Solve the following math question.
Answer the following math question.
Table 2.Task Description snippets for the system message.
Closers
None
This will be fun!
Take a deep breath and think carefully.
I really need your help!
Table 3.Closing snippets for the system message.
3.1.Dataset
Careful selection of the dataset for testing against constitutes a critical aspect of this study. Our aim was to identify a challenging task that was unlikely to have been directly encountered during the training of the models4. While our preference was for an internal dataset specific to VMware, the absence of large-scale datasets with directly quantifiable scoring metrics, such as accuracy or F1, necessitated the utilization of a publicly available benchmark dataset. Ultimately, we opted for GSM8K (Cobbe et al., 2021). Given the ongoing limitations of contemporary LLMs, particularly in addressing basic mathematical tasks, especially those involving multi-step reasoning, we deemed GSM8K an optimal choice for illustrating the impact of seemingly inconsequential augmentations to the prompt’s system message.
3.2.Scoring
In the context of GSM8K’s mathematical assessment, we adopted a stringent approach to scoring that precluded the assignment of partial credit. Thus, we employed Exact Match (EM) as our scoring metric. The model was evaluated based on whether it correctly provided the exact numerical solution or not. This rigorous methodology ensures a clear and unambiguous assessment of the model’s accuracy in providing the exact numerical output.
3.3.Output Parsing
Given the unforgiving nature of EM scoring, it is essential to note that, despite the answer to GSM8K questions being numerical, the output of an LLM is a string. Consequently, meticulous attention must be paid to the formatting and parsing of the non-numerical output. From the standpoint of string equality, it is imperative to recognize distinctions such as the string “30000” not being equivalent to “30,000” or “30000.00”. To mitigate this challenge, a post-processing step was implemented to ensure accurate scoring by preventing misclassification of a response as incorrect when it was, in fact, accurate.
3.4.Scale
Benchmark datasets typically encompass thousands of examples in their test sets; GSM8K has over 1,300 examples in its test set. Such an extensive scale of data is exceptionally uncommon in real-world datasets, particularly during the initial stages of a project. To replicate this rarity, we systematically subset the test set of GSM8K, extracting subsets containing the first 10, 25, 50, and 100 questions, thereby allowing us to illustrate the impact of “positive thinking” as the dataset size increases. Notably, we limited our experiments to a maximum of 100 questions to mitigate computation time, as computing results for the entire test set would have required weeks and incurred a substantial carbon cost for what would likely be diminishing returns.
3.5.Model Selection
Although we aspired to assess widely recognized commercial models such as GPT-3.5/4, Gemini, Claude, etc., conducting experiments involving 12,000 requests per model was deemed financially prohibitive, as it would have incurred costs amounting to many thousands of dollars. Consequently, we opted to utilize models hosted by VMware NLP Lab’s LLM API. Specifically, our evaluations were conducted on Mistral-7B5 (Jiang et al., 2023), Llama2-13B6 (Touvron et al., 2023), and Llama2-70B7 (Touvron et al., 2023).
Model Number of Questions Chain of Thought EM Baseline ↑ EM Mean ↑ EM Std Dev ↓ EM Min ↑ EM Max ↑
Mistral-7B 10 No 0.10 0.1000 0.0000 0.10 0.10
Mistral-7B 25 No 0.08 0.0800 0.0000 0.08 0.08
Mistral-7B 50 No 0.12 0.1197 0.0026 0.10 0.12
Mistral-7B 100 No 0.09 0.1053 0.0072 0.08 0.11
Mistral-7B 10 Yes 0.20 0.3800 0.0659 0.20 0.50
Mistral-7B 25 Yes 0.28 0.3660 0.0453 0.28 0.48
Mistral-7B 50 Yes 0.32 0.3890 0.0254 0.32 0.44
Mistral-7B 100 Yes 0.35 0.4030 0.0183 0.35 0.44
Llama2-13B 10 No 0.10 0.1000 0.0000 0.10 0.10
Llama2-13B 25 No 0.08 0.0853 0.0137 0.08 0.12
Llama2-13B 50 No 0.08 0.0827 0.0069 0.08 0.10
Llama2-13B 100 No 0.07 0.0713 0.0034 0.07 0.08
Llama2-13B 10 Yes 0.40 0.3967 0.0258 0.30 0.50
Llama2-13B 25 Yes 0.44 0.4513 0.0234 0.40 0.52
Llama2-13B 50 Yes 0.46 0.4657 0.0117 0.44 0.50
Llama2-13B 100 Yes 0.47 0.4542 0.0162 0.41 0.49
Llama2-70B 10 No 0.10 0.1000 0.0000 0.10 0.10
Llama2-70B 25 No 0.20 0.1273 0.0173 0.12 0.20
Llama2-70B 50 No 0.20 0.1637 0.0086 0.16 0.20
Llama2-70B 100 No 0.18 0.1627 0.0048 0.16 0.18
Llama2-70B 10 Yes 0.60 0.5867 0.0343 0.50 0.60
Llama2-70B 25 Yes 0.64 0.6380 0.0270 0.60 0.68
Llama2-70B 50 Yes 0.60 0.6190 0.0259 0.56 0.68
Llama2-70B 100 Yes 0.63 0.6617 0.0179 0.62 0.70
Table 4.Performance statistics for subsets of the GSM8K test set across 60 “positive thinking” prompt combinations, with and without Chain of Thought.
3.6.In-Context Learning
Initially, our intent was to abstain from incorporating examples in the prompt; however, this approach proved ineffective in eliciting the desired response format from the model. Given the nature of these models, specifically that they were designed for conversational interactions, achieving success in terms of Exact Match (EM) scoring necessitated guiding the model to refrain from generating a response comprising multiple sentences. To accomplish this, we resorted to incorporating examples via in-context learning (Brown et al., 2020), as exposure to instances of the desired output format significantly increased the likelihood of the model producing responses aligned with the specified format (though, as previously mentioned, significant post-processing was still required to get the simple numerical response).
To maintain experimental consistency by minimizing the number of variables that changed in each iteration, we adopted an intentionally naive strategy for in-context learning. Strategies such as K-Nearest-Neighbor (KNN) example selection have been shown to increase model performance (Liu et al., 2021); however, we chose not to employ any such strategies, so as to hold the number of variables changing per experiment to one: the modified system message only. Specifically, we limited the examples to the last four instances from the test set8, thereby providing a consistent and focused set of samples for the model to learn from. Notably, four examples emerged as the minimum number required to consistently elicit the correct output format.
3.7.Automatic Prompt Optimization
Engaging in the iterative process of refining prompts and monitoring the subsequent score progression can be an enjoyable endeavor. However, this approach proves to be highly time-inefficient, especially when systematically assessing all modifications from a scientific standpoint. Existing research, as demonstrated by Yang et al. (Yang et al., 2023), highlights the superior capability of LLM systems in optimizing their own prompts compared to human efforts. In light of this, we conducted a comparative analysis pitting human-generated “positive thinking” optimization against the utilization of DSPy (Khattab et al., 2023) Optimizers9 at the same question subsets: 10, 25, 50, and 100.
It is noteworthy that the questions utilized for optimization were additional and distinct from the evaluation set and the in-context learning examples, though also originating from the end of the test set. For the most extensive trial, 100 “new” questions were employed for the optimization process, while the same 100 evaluation questions were used for the evaluation processes, so as to make the scores directly comparable. Importantly, each model was exclusively employed to optimize itself; cross-model optimizations, such as using Llama2-70B to optimize the prompt for Mistral-7B, were not pursued.
4.Experimental Results
As evidenced in the subsequent sections, certain overarching patterns become apparent; however, they do not universally apply to each model across all prompting strategies. We will explicitly illustrate that there is no straightforward universal prompt snippet that can be added to optimize any given model’s performance.
For these experiments, baseline performance refers to the scenario where the model receives no system message, signified by the opening snippet, task description, and closing snippet all being designated as “None”. For Sections 4.1-4.3, refer to Table 4.
Number of “Positive Thinking” Automatic Optimizer
Model Questions OS EM ↑ ES EM ↑ Avg EM ↑ EM Delta ↓ OS EM ↑ ES EM ↑ Avg EM ↑ EM Delta ↓
Mistral-7B 10 0.30 0.50 0.400 0.20 0.60 0.20 0.400 0.40
Mistral-7B 25 0.32 0.48 0.400 0.16 0.52 0.24 0.380 0.28
Mistral-7B 50 0.40 0.44 0.420 0.04 0.50 0.34 0.420 0.16
Mistral-7B 100 0.23 0.43 0.330 0.20 0.43 0.39 0.410 0.04
Llama2-13B 10 0.30 0.50 0.400 0.20 0.50 0.50 0.500 0.00
Llama2-13B 25 0.28 0.48 0.380 0.20 0.48 0.44 0.460 0.04
Llama2-13B 50 0.30 0.46 0.380 0.16 0.48 0.38 0.430 0.10
Llama2-13B 100 0.25 0.47 0.360 0.22 0.40 0.46 0.430 0.06
Llama2-70B 10 0.40 0.60 0.500 0.20 0.50 0.40 0.450 0.10
Llama2-70B 25 0.52 0.68 0.600 0.16 0.60 0.64 0.620 0.04
Llama2-70B 50 0.44 0.68 0.560 0.24 0.66 0.52 0.590 0.14
Llama2-70B 100 0.39 0.70 0.545 0.31 0.61 0.60 0.605 0.01
Table 5.Performance results for the best “positive thinking” prompts compared to automatically optimized prompts. “OS EM” is Exact Match on the Optimization Set. “ES EM” is Exact Match on the Evaluation Set. “Avg EM” is the average of the Exact Match for the two sets. Bold is for the higher Average EM. “EM Delta” is the difference between the Exact Match for the two sets. An underline is for the lower EM Delta. All prompts are with Chain of Thought.
4.1.Mistral-7B Results
Without Chain of Thought prompting, Mistral-7B’s performance remained remarkably consistent across all prompt permutations. At both the 10 and 25 question sets, there was no deviation. Even for the 100 question subset, the maximum observed standard deviation was a mere 0.007. The variability observed at 50 questions appears to be an anomaly. Examination of the results in Appendix B.3 reveals that, with the exception of one prompt scoring 0.10, all others scored 0.12. It is unclear why this particular prompt led to one additional incorrect response compared to the other 59 prompt variations. In contrast, the results for 100 questions in Appendix B.4 demonstrate a reasonable spread between 0.08 and 0.11. In relative terms, Mistral-7B, when prompted without Chain of Thought, exhibits substantial prompt invariance, with the “positive thinking” prompts only matching or marginally surpassing the baseline.
This trend reverses when Mistral-7B is prompted with Chain of Thought. Instead of observing a slight increase in deviation with the number of questions, there is a steady and substantial decrease, ranging from 0.066 at 10 questions to 0.018 at 100 questions. In this scenario, “positive thinking” prompts significantly outperformed the baseline with no prompts falling below the baseline. Please refer to Appendices B.5 through B.8 for the ranked order of prompts for Mistral-7B with Chain of Thought.
4.2.Llama2-13B Results
Without Chain of Thought prompting and ignoring the 10 question set where there was no deviation, Llama2-13B shows an opposite trend to Mistral-7B, with deviation decreasing from 0.014 at 25 questions to 0.003 at 100 questions. While slightly less stable than Mistral-7B, without Chain of Thought, LLama2-13B is also fairly prompt invariant, with the “positive thinking” prompts again only matching or marginally exceeding the baseline.
With Chain of Thought prompting, the trend is less clear. It does overall decrease from 0.026 at 10 to 0.016 at 100, but, at 50, it’s even lower, at 0.012. This is the only case where that occurred. For Mistral-7B, and as we’ll show in the next section with Llama2-70B, variation consistently decreased as the number of questions increased when employing Chain of Thought prompting.
4.3.Llama2-70B Results
Without Chain of Thought prompting and ignoring the 10 question set where there was again no deviation, Llama2-70B exhibits a similar trend to Llama2-13B, with deviation again decreasing from 0.017 at 25 questions to 0.050 at 100 questions. Across all three models, the prompt variance was an order of magnitude lower without Chain of Thought when compared to using Chain of Thought at the same question count. However, in terms of actual performance, the “positive thinking” prompts all matched or underperformed baseline. This is a stark departure from the pattern seen with Mistral-7B and Llama2-13B.
That departure does not persist when employing Chain of Thought prompting. While the performance of the “positive thinking” prompts with Chain of Thought did underperform the baseline on average for 10 and 25 questions, it outperformed the baseline on average for 50 and 100 questions. As for variance, the general pattern of standard deviation decreasing with question count does hold.
4.4.General Trends in Results
It’s challenging to extract many generalizable results across models and prompting strategies, as every nearly evident trend we observed had at least one notable exception. In fact, the only real trend may be no trend. What’s best for any given model, dataset, and prompting strategy is likely to be specific to the particular combination at hand. Thus, we turned from hand-tuning the system message with optimistic “positive thinking” to automatic prompt optimization.
4.5.Automatic Prompt Optimization Results
As anticipated, the prompts that underwent automatic optimization consistently equaled or surpassed the effectiveness of our manually generated “positive thinking” prompts in nearly all instances. The instances where “positive thinking” achieved higher average scores across the optimization and evaluation sets were limited to Mistral-7B with 25 questions and Llama2-70B with 10 questions. However, evaluating performance solely on raw scores is insufficient; hence, we also examined the delta between scores on the optimization set and the evaluation set. A lower delta implies superior generalization of the prompt. Therefore, the optimal strategy combines the highest average score with the lowest delta. See Table 5 for the performance comparison.
For Mistral-7B, the results present a mixed scenario. “Positive thinking” exhibits a lower delta for 10, 25, and 50 questions, while the automatically optimized prompt demonstrates a lower delta for 100 questions. Considering Mistral-7B’s model capacity, it is understandable that it faces challenges in optimizing its own prompt when compared to the larger Llama2-13B and 70B models. In contrast, for both Llama2-13B and 70B models, the automatically optimized prompts consistently show a lower delta across all cases. Consequently, it is advisable to refrain from manually fine-tuning prompts when using models larger than 7B, and instead, leverage the model’s ability to autonomously optimize prompts. For 7B models, more work is required to see if the trend of automatically optimized prompts outperforming manually tuned prompts holds for sample sizes exceeding 100 questions.
This recommendation aligns with the original auto-optimization paper. However, the noteworthy aspect lies in the nature of the optimized prompts themselves. They diverge significantly from any prompts we might have devised independently. If presented with these optimized prompts before observing their performance scores, one might have anticipated their inadequacy rather than their consistent outperformance of hand-tailored prompts. A prime example is illustrated by the highest-scoring optimized prompt and prefix generated by Llama2-70B for the 50-question subset:
System Message:
«Command, we need you to plot a course through this turbulence and locate the source of the anomaly. Use all available data and your expertise to guide us through this challenging situation.»
Answer Prefix:
Captain’s Log, Stardate [insert date here]: We have successfully plotted a course through the turbulence and are now approaching the source of the anomaly.
Surprisingly, it appears that the model’s proficiency in mathematical reasoning can be enhanced by the expression of an affinity for Star Trek. This revelation adds an unexpected dimension to our understanding and introduces elements we would not have considered or attempted independently. For a comprehensive collection of the peculiar and fascinating prompts generated by the three models, refer to Appendix C.
5.The Reproducibility Problem
Although somewhat peripheral to the primary research question addressed in this paper, it is noteworthy that our findings exhibit significant discrepancies from the published performance scores of Mistral-7B and Llama2-13B, whereas Llama2-70B fell within an acceptable margin of error, considering our evaluation was conducted on approximately 15% of GSM8K’s test set. Refer to Table 6 for the score comparisons.
The most significant deviation was observed in the case of Llama2-13B. Meta reported a score of 0.29 on the GSM8K dataset. Our results without Chain of Thought yielded a score of 0.07, whereas with Chain of Thought, we achieved a score of 0.43. Due to both Meta and Mistral AI’s omission of the prompts used for testing their models, we can only speculate about the reasons behind our substantial performance differences compared to their reported scores.
This instance underscores a broader issue of reproducibility that has long existed inside the machine learning community, but has become significantly exacerbated since the advent of LLMs. Without the publication of prompts employed by researchers with their models, reproducing their results becomes a formidable challenge. As shown in this paper, trivial variations in the prompt can have dramatic performance impacts. We implore all future research publications to include the prompts used in an appendix. Refer to Appendix A to see our prompt templates.
Model Reported EM Our EM@100 Delta
Mistral-7B 0.52 0.41 −0.11
Llama2-13B 0.29 0.43 +0.13
Llama2-70B 0.57 0.61 +0.04
Table 6.Comparing the reported scores for each model as reported by the model publishers on the whole GSM8K test set against the best average score we were able to achieve across our optimization and evaluation subsets, which accounted for about 15% of the test set.
6.Future Work
This work could easily be expanded with additional prompt variants, models, and datasets. However, due to the combinatorial complexity of scientifically testing each change, manual prompt engineering is far from the most efficient methodology for improving model performance. Instead, the best path forward is to use libraries like DSPy to apply a more structured approach to constructing LLM-powered applications and use the built-in optimizer to automatically tune the prompt for your dataset and model of choice.
7.Conclusion
It’s both surprising and irritating that trivial modifications to the prompt can exhibit such dramatic swings in performance. Doubly so, since there’s no obvious methodology for improving performance. Affecting performance is trivial. Improving performance, when tuning the prompt by hand, is laborious and computationally prohibitive when using scientific processes to evaluate every change.
In this paper, we showed that you don’t need massive commercial models like PaLM 2 or GPT-4 to tune your prompt. Mistral-7B struggled to optimize its own prompt until it had 100 questions to work with. Llama2-13B and 70B were able to produce superior prompts with as little as 10 questions to optimize with. And while the prompts they generated may appear shocking to an experienced practitioner, it’s undeniable that the automatically generated prompts perform better and generalize better than hand-tuned “positive thinking” prompts.
Acknowledgements.
We would like to thank the entire VMware NLP Lab and AI Platform Team for supporting this effort, Ramesh Radhakrishnan for reviewing the paper, and Omar Khattab for his suggestions and guidance.
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