Venue: ICLR 2023 TinyPapers

Reviews:

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 introduces the task of text-to-figure generation, which involves creating scientific figures for papers based on textual descriptions. The authors propose a diffusion-based approach to tackle this problem and discuss some challenges.
strengths_and_weaknesses: Strengths:
1. The paper presents a diffusion-based approach for the text-to-figure generation task, showcasing its applicability in this domain.
2. The authors conduct extensive experiments, evaluating the performance of various text encoders and different scales of the CFG.
3. Code and models will be made public.

Weaknesses:
1. The core findings of the paper are not clearly presented. It would have been helpful if the authors had provided a comparison between existing models.
2. The paper would benefit from additional evaluation metrics, such as OCR-SIM. The FID metric is primarily statistical, and considering the importance of text in scientific figures, incorporating more text-centric metrics would provide a more comprehensive evaluation.
suggested_changes: See weakness. 
Typos: 
1. In Table 1, "ISC" or "IS"?
review_title: null

Decision: Invite to present