Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: This paper presents a study on prompt programming for visual image generation
strengths_and_weaknesses: ### Weaknesses ###
- Clarity: The motivation behind this study is not entirely clear, although it points towards a more systematic analysis of prompt engineering for visual image generation. 
- Correctness: Given the lack of clear direction in the paper, I am not sure how correct and relevant the results are to the core motivation behind the paper.


suggested_changes: The authors need to place their work more specifically within the realm of either prompt engineering and describe exactly what novel contribution does their work offer to the field.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: This paper examines the influence of different kinds of prompts on text to image models. Specifically, they look at how different kinds of prompts affect the visual fidelity of the resulting images.
strengths_and_weaknesses: Strengths:
- Clarity: Relevant related work is well discussed, findings are communicated somewhat clearly

- Follows basic requirements: Paper is within the page limits and complies with formatting requirements

Weaknesses:
- Correctness: Missing details on how the experiments justify and support the claims made

- Reproducibility: Missing some details on how the experiment was done
suggested_changes: Thank you for your submission! I think the idea of looking at the impact of different prompts on text to image models is very interesting. 

Overall I think this paper is a great start, but I felt that it would benefit from some more details surrounding the experimental setup and conclusions of the findings. These additions would allow for better reproducibility of your experiments and the ability to asses the correctness of the claims made in the paper. I think these two points go together, because the better your experiments can be understood, the easier it is to asses the correctness of the findings. 

For the details surrounding the experimental setup, it would be great to include more about what your experiment did. For example, how did you choose prompts? How did you then interact with the models you chose once you decided your prompts? Why did you choose tomatoes, wheat, a robot, and fungi? What is the significance of the exclamation point in the prompts?

The conclusion is that creative prompting makes a difference on the visual fidelity of the images generated, but it would be helpful to add some context on how you came to this conclusion and why your experiments support this conclusion. For example, why do the images in Figure 1 tell me that the type of prompt makes a difference? Since each prompt was in a different meta-prompt style but was also asking to draw different objects, it's hard to tell if the meta prompt styles made a difference. Also, why is it enough to draw conclusions from just one image per meta prompt?
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: The authors ask how llm's for text to image synthesis can probe imagery that embodies fidelity and imagination using prompt programming.
strengths_and_weaknesses: Strengths:
- The paper is well-written and clear.

Weakness:
- The technical analysis should be stronger to support the assessments made in section 4.
- Reproducibility could be further improved upon.
suggested_changes: Changes:
- In page 1, it is mentioned that "We begin our exploration by referencing six templates..". Three of them from (Reynolds & McDonnell) have been mentioned below. It's a bit unclear what the other 3 are?
review_title: null

Decision: Invite to revise