Detecting Winning Arguments with Large Language Models and Persuasion Strategies
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A new study explores persuasion strategies in argumentative texts, utilizing large language models (LLMs) to enhance predictions of text persuasiveness. By analyzing three annotated datasets, including Winning Arguments from the Change My View subreddit, researchers found that strategy-guided reasoning significantly improves assessment accuracy. They also released a topic-annotated version of the Winning Argument dataset to aid future research in this area.
New Research Leverages Large Language Models to Detect Persuasive Arguments
Recent studies have revealed advancements in utilizing large language models (LLMs) to identify persuasive strategies within argumentative texts. This research focuses on specific strategies like reputation attacks, distraction techniques, and manipulative wording.
The study explores three annotated argument datasets: Winning Arguments, derived from the Change My View subreddit, Anthropic/Persuasion, and Persuasion for Good. The researchers employed a Multi-Strategy Persuasion Scoring method, which improves the prediction of a text's persuasiveness.
This research highlights the efficacy of structured, strategy-aware prompting and emphasizes the need for greater interpretability in assessing argument quality. The team has made the topic-annotated version of the Winning Arguments dataset publicly available, aiming to support future research.
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📰 Original Source: https://arxiv.org/abs/2601.10660v1
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