Prompt Exploration with Prompt Regression

Summary

This paper investigates how to select the optimal prompt from a prompt library given a specific model and a task. The authors propose a prompt regression approach, which utilizes task datasets to fit prompt weights, thereby achieving prompt selection. The authors design regression methods based on log-probability and preference for two types of task datasets, respectively. Overall, the methods proposed in the paper outperform baseline models, demonstrating their effectiveness.

Rating

6

Confidence

3

Ethics flag

1

Reasons to accept

- The proposed methods are reasonable, and the results demonstrate their effectiveness. - The proposed method is derived from regression theory and is supported by mathematical definitions and formulations.

Reasons to reject

- The effectiveness of the methods may vary depending on the nature of the task. - The proposed method didn't compare with other related works, comparing only with its baselines. - In some cases, even random prompt combinations can outperform both versions of PEPR.

Questions to authors

- The objective of the proposed methods is to learn prompt weights through regression conducted on specific datasets. Once these weights are obtained, they can be applied to each data point in the test set. My concern is whether this approach is task-specific. Do we need to know which task category the input belongs to in order to apply the corresponding prompt weights? If so, does this mean that such methods primarily apply to narrow-domain tasks, while being unsuitable for open-domain dialogue scenarios? - During training, are multiple datasets mixed for training, or are they trained separately? - Table 2 should be placed on page 8. - From the results in Table 2, it seems that the effectiveness of PEPR may be related to the dataset. For some datasets, such as HateCheck and NI 199, even the "random" prompt achieves good results. Although the authors also provide some explanations for this phenomenon in the Discussion section, is it possible that certain tasks are insensitive to prompts? - Previous studies have shown that large language models commonly suffer from overconfidence issues, wherein even incorrect or illusory generated results are assigned a high probability. Could this potentially affect methods based on log-probability? - Please provide more experimental details for both the prompt regression and prompt selection to make it more clear and understandable. - Please explain how the experimental results in Figure 2 validate the assumption that " the elimination or addition of an element from the prompt library does not affect interactions between the other prompts." - Typo - Introduction section, " rigorously in the following sections," => "rigorously in the following sections." and "selection components of PEPR," => "selection components of PEPR."

Summary

The paper tackles a specific prompt engineering problem for LLMs, aiming to optimize prompt combinations from a library of individual elements. The goal is to predict the effects of these combinations on LLM outputs and create an optimal prompt for specific tasks. The proposed solution, Prompt Exploration with Prompt Regression (PEPR), consists of three steps: building a prompt library, deriving weights for each element through prompt regression, and selecting prompt elements via these weights. This process incorporates generated texts or human preferences to align prompts with the desired outcomes. PEPR's effectiveness is validated using several open-source LLMs across different datasets and tasks.

Rating

6

Confidence

4

Ethics flag

1

Reasons to accept

1. The paper is commendable for its comprehensive review and summarization of up-to-date related works, effectively highlighting novelty of the paper. 2. The quality of writing in this paper is excellent. It is exceptionally clear and well-structured, making it easy for readers to understand and follow the arguments and methodologies presented. 3. The authors have included a detailed appendix which enriches the paper further. This appendix serves as a valuable resource for readers seeking a deeper understanding of the paper's contributions and the intricacies of the methodologies used.

Reasons to reject

1. The central concern of the paper is the prompt library search problem within prompt engineering, which seems under-motivated. The quality of the final prompt is inherently limited by the quality of the constructed prompt library, particularly in terms of coverage and richness. Moreover, PEPR's approach to determining the weight of each prompt element through parameter optimization on specific data significantly constrains the generalizability of the resultant prompts. Additionally, the paper does not adequately discuss the scale of data used for parameter optimization, leaving PEPR's practical applicability unclear. 2. The evaluation and rationalization of PEPR's effectiveness are incomplete. The experiments on prompt regression merely test the assumption of the independence of irrelevant alternatives, while those on prompt selection only assess PEPR's impact on task performance under various settings. Crucially, there is a lack of direct comparison between PEPR and the advanced prompt engineering methods described in the related work section. Such comparisons are vital to directly demonstrate PEPR's superiority and the relevance of analyzing prompt element combinations for producing the optimal overall prompt as outlined in the initial sections of the paper. 3. More importantly, current experimental results (referenced in Table 2) show that PEPR barely outperforms or does not surpass the random baseline. As expressed in the last paragraph of section 4.2, even with performance metrics variations across different datasets, these outcomes further challenge the significance and motivation behind the paper's focus on the prompt library search problem.

Questions to authors

1. In the prompt selection experiments illustrated in Figure 2, regression performance deteriorates as the number of prompt elements increases, indicated by larger MAE and smaller r-values. What could be the underlying reasons for this trend? Does this imply that an increase in the number of prompt elements leads to more coupling between them, thereby reducing their independence? Moreover, does the extent of this coupling depend on the quality of the prompt library? 2. What exactly is the "minimally-prompted model" mentioned in the second paragraph of section 4.2? Why was this baseline chosen instead of more advanced methods summarized in the related work section? What are the implications of using such a baseline for the validity of the experimental results?

Ethics concerns details

N/A

Summary

The paper introduces a framework, Prompt Exploration with Prompt Regression (PEPR), which aims to optimize the selection of prompt elements for language models. It claims that unlike prior methods, PEPR can predict the output effects of various prompt combinations without exhaustive testing. The authors present a methodology involving building a library of prompt elements, assigning weights through regression analysis, and selecting the optimal combination based on these weights. The paper asserts that its approach, tested on various datasets and model sizes, improves prompt selection efficiency and effectiveness. It suggests that PEPR could significantly streamline the process of prompt engineering, although it also calls for further research to refine and expand the method. Overall, the paper is well written and introduces an approach for which the COLM conference is arguably the perfect venue. Hence, we believe it should be accepted.

Rating

8

Confidence

4

Ethics flag

1

Reasons to accept

- PEPR provides a method to predict the outcomes of combined prompt elements without testing each combination, addressing the inefficiency of brute-force methods by providing a more principled method of building prompts from a selection of templates. - The framework integrates regression analysis to assign quantitative impacts to individual prompt components, allowing for systematic optimization. - PEPR utilizes both reference text generations and preference data, offering flexibility in aligning language model outputs with human judgments or desired behaviors. - The method is validated across multiple open-source language models of the Llama2 family and four diverse tasks, showing its adaptability and broad potential for future utilization. - PEPR introduces a novel approach to prompt library search, a previously underexplored area in language model research.

Reasons to reject

- The paper does not fully address the scalability of PEPR with very large prompt libraries or complex prompt structures, which could limit its practicality in extensive applications. - The methodology may not account adequately for the nuanced interactions between prompt elements beyond their individual contributions, potentially oversimplifying complex linguistic phenomena. - Further, the methodology relies heavily on the initial quality and composition of the prompt library, potentially limiting its effectiveness if the library is not well-constructed. - The experiments lack a comparison against a wider range of existing prompt engineering methods, which could help in understanding PEPR's relative performance. All in all, these reasons are relatively superficial and do not warrant the rejection of the paper. Moreover, the authors address most of them in the manuscript to a great extent.

Questions to authors

1. Can you shed further light on what you mean by "solving (3.3) does not require knowledge of the reference (correct) y for the inputs (e.g., when there is a finite set of meaningful generations (such as classification or multiple-choice QA), one can simply plug every possible y for every input xi when fitting the regression coefficients)". In a quick reading it might sound like the PEPR method only supports classification usecases but that is patently not the case when considering the rest of the paper. 2. Would it be possible to quantify the level of interaction between prompt elements when their effects on language model outputs are not independent and make use of this information in the prompt selection step?

Summary

This work studies the prompt library search problem where the model needs to predict prompt combinations in a library that can lead to optimal performance. This work further proposes a method including three steps: (1) building a prompt library; (2) generating weight for each element; (3) prompt selection based on weights.

Rating

8

Confidence

3

Ethics flag

1

Reasons to accept

- The problem formulation of prompt library search is novel and interesting but could benefit from more explanation of its motivation and practical usage. - This work provides well-formulated mathematical definitions and formulae for prompt regression and prompt selection. - This framework enables quantitative analysis of individual prompt component's impact in regression analysis. - The experiments are conducted across a wide range of language models.

Reasons to reject

- There is a lack of comparison with existing prompt engineering methods. The existing comparison with a random baseline is not enough to show the advantage of the proposed PEPR method. - The performance of this method highly relies on the quality of the prompt library.

Discussion period is now open

Hi reviewers, please take a look at the author's rebuttals and the other reviews for this paper! If the rebuttals addressed your concerns, please let the authors know about this and update your review. If not, please continue to engage with the authors and the other reviews in the discussion forum. There is significant disagreement among the reviewers here. jCnP, you are the most negative about this paper, particularly with concerns about the limitations of using a prompt library constructed a priori, and a lack of experiments comparing with prior methods, plus a lack of significant improvement over baseline methods. Did the authors' response address these concerns? t5Qb and cEye, you are both quite positive about the paper. Would you like to argue for its acceptance?

Response to Authors' Rebuttal

Thank you for addressing my concerns. Your responses clarified the motivation for the prompt library search problem and demonstrated the generalizability of PEPR prompts across related tasks. You also provided useful information on the scale of data used for optimization and the rationale behind the "minimally-prompted model." However, some issues remain unresolved, particularly regarding the completeness of comparisons to advanced methods. While I appreciate the effort and the improvements in understanding, the responses still leave some questions about methodological details. Therefore, I have decided to increase the score from 4 (reject) to a borderline accept, reflecting the clarifications provided and the potential of your approach. Further rigorous validation would enhance the confidence in the findings.