Generating free-text rationales is among the emergent capabilities of Large Language Models (LLMs). These rationales have been found to enhance LLM performance across various NLP tasks. Recently, there has been growing interest in using these rationales to provide insights for various important downstream tasks. In this paper, we analyze generated free-text rationales in tasks with subjective answers, emphasizing the importance of rationalization in such scenarios. We focus on pairwise argument ranking, a highly subjective task with significant potential for real-world applications, such as debate assistance. We evaluate the persuasiveness of rationales generated by nine LLMs to support their subjective choices. Our findings suggest that open-source LLMs, particularly Llama2-70B-chat, are capable of providing highly persuasive rationalizations, surpassing even GPT models. Additionally, our experiments show that rationale persuasiveness can be improved by controlling its parameters through prompting or through self-refinement.

分析了使用大型语言模型生成的自由文本解释对于主观答案的重要性，并评估了九个开源语言模型生成的自由文本解释的说服力，结果显示主观排序任务中Llama2-70B-chat模型的解释具有高度说服力，超过了GPT模型，并发现通过引导或自我改进可以提高解释的说服力。

生成的自由文本理由在主观决策中的说服力：一项关于两两论证排名的案例研究