Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking (2024)

Abstract: 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 demonstrate that the persuasiveness of the generated rationales can be enhanced by guiding their persuasive elements through prompting or self-refinement techniques.

## introduction large language models (llms) @xcite @xcite have demonstrated a strong ability to generate free-text rationales to explain and support their decisions in plain natural language, which adds an essential layer of transparency and interpretability to their outputs. recently, there has been a growing interest in utilizing these rationales to enhance the usability and reliability of llm-based applications, thereby reducing the risks posed by llms in decision-making processes @xcite . existing research on evaluating and analyzing free-text rationales has primarily focused on tasks where there is an expected factual ground truth ## rationale evaluation figure 2 outlines our evaluation process, which consists of three key stages: (1) basic-form evaluation: this initial stage filters out meaningless rationales, ensuring only valid ones proceed for further analysis, similar to the concept of surfaceform evaluation @xcite . (2) content evaluation: we assess the rationale's content by analyzing its support through contrast and its informativeness through novelty, aiming to understand how rationale content influences its persuasiveness. we rely on human annotators to evaluate each stage, using the 270 rationale subset from the ibm-9k described in we use mechanical turk workers for annotations at each evaluation stage. each dimension in basic form and content evaluation is assessed with yes/no questions, with the majority vote of three workers determining the final decision on contrast and novelty. for persuasiveness, rationales are evaluated through pairwise comparisons, where three workers vote on whether a rationale is more, less, or equally persuasive compared to another. workers also write 1 -2 sentences explaining their choices to ensure critical thinking. rigorous quality assurance methods ensure annotation reliability. 6 analysis type examples input argument example topic: "flu vaccination should not be mandatory" argument 1: "the state ought not intervene in individuals' right of bodily autonomy regardless of societal harms. forcing vaccinations is a crude invasion to someone's body and should therefore not be done." argument 2: "forcing people to participate in a system they do not trust, often with passion and for irrational reasons, might lead them to distrust conventional medicine as a whole, increasing the risk to their health." decision: "argument 1". ## conclusion and future work this paper presents a comprehensive analysis of the persuasiveness of free-text rationales generated by various llms. our results show that open-source models, particularly llama2-70b-chat, generate highly persuasive rationales, surpassing strong closed-source gpt models. while gpt4's rankings generally align with human judgments, discrepancies arise due to the task's inherent subjectivity. we proposed a detailed human evaluation studying key factors contributing to persuasiveness. we found that contrastive rationales,where the model justifies its choice and refutes the alternative, the most significant. we also demonstrated that prompting models with specific persuasiveness parameters enhances rationale persuasiveness. future work will explore the user acceptance of model-chosen arguments and investigate other subjective tasks beyond pairwise argument ranking.