BloomWise: Enhancing Problem-Solving capabilities of Large Language Models using Bloom’s-Taxonomy-Inspired Prompts
Authors: Maria-Eleni Zoumpoulidi, Georgios Paraskevopoulos, Alexandros Potamianos


Reviewer paper summaries: This paper present bloomise, which can be a replacement to prompt LLM to solve math problem. Bloom is composed of six components. Bloom achieve reasonable performance on four math reasoning datasets.
Summary of strengths: The method is clearly presented and the whole paper is easy to follow.
Bloom has some performance improvement over cot.
Bloom achieves competitive performance under oracle setting.
Summary of weaknesses: First, the performance is mixed. There is no consistent improvement over baselines.
And authors do not compare include ToT in the baselines, which is an important baseline.
In addition, there is no discussion about complexity of the method, which is important.
Finally, the evaluation is not enough case all of the datasets are not large enough, you should consider MATH.
Comments, suggestions and typos: line 373: The first concerns the study and commentary of the results by Bloom taxonomy level. (concern)
line 427: the text of table should end with period
line 533: Last but not least, adding the LLM replies from the previous levels of Bloom’s taxonomy (level)
Ensure consistent use of "LLM" and "LLMs" throughout the paper
Overall assessment scores: 2.5