Exploring the Role of Semantic Parsing on Downstream Tasks for Large Language Models
Authors: Kaikai An, Shuzheng Si, Helan Hu, Haozhe Zhao, Yuchi Wang, Qingyan Guo, Baobao Chang


Reviewer paper summaries: This paper shows that by simply adding "please use semantic parsing result which can enhance comprehension of the sentence’s structure and semantic" (with some variations) to the prompt, the LLM can improve its' performance on many NLP tasks such as Translation, Simplification Paraphrase and classification. Meanwhile, adding actual semantic parsing result into the prompt might hurt.
Summary of strengths: 1. Clear result and simple method.
Summary of weaknesses: 1. GPT4 or later models might understand semantic parsing result better. (maybe in the image form)
2. Since LLM is not asked to produce its' understanding, this Sense prompt is not structually different from other magic prompts such as "thinking step by step".
Comments, suggestions and typos: What would happen if we ask LLMs to actually produce semantic parsing result and then rely on it? 
I quickly tested this with different LLMs, they seem to produce different semantic structure
Overall assessment scores: 4