Paired-Prompts: Guiding LLM to Make Better Summarization  in Fine-Tuning
Authors: Kehan Fan, Junchao Wang, Zhiheng Huo


Reviewer paper summaries: The paper introduces the Paired Prompt technique, which integrates Fine-tuning and Prompt Engineering within Large Language Models (LLMs). This approach is rooted in Instruction Tuning, leveraging distinct prompts during the training and inference stages to unlock the full potential of LLMs for specific tasks. The authors conducted experiments on the dialogue summarization task. The findings reveal that the proposed method demonstrates strong generalization capabilities and significantly outperforms traditional fine-tuning methods.
Summary of strengths: Results show improvements for paired prompts-based fine-tuning approaches over single prompt-based fine-tuned approaches.
Summary of weaknesses: No discussion regarding how to choose "simpler prompts" during inference phase general tasks. Generalizing to other tasks is not established in the paper.
Comments, suggestions and typos: 1. Paper missing line numbers? Not according to ACL Guidelines? 
2. Introduce the PLM acronym before discussing it (Section 2.1)
3. Typo in Section 2.3
Overall assessment scores: 2