Topic-Guided Prompting for Argument Stance Classification

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new Topic-Guided prompting method has been developed for argument stance classification, a critical task in argument mining and subjectivity analysis for understanding public discourse. This method addresses the sensitivity of Large Language Models (LLMs) to prompt construction by dynamically integrating topic-specific information into the few-shot context. Evaluated across five LLMs and three datasets, including formal debates and user-generated online comments, the Topic-Guided prompting significantly outperforms both standard few-shot prompting and leading example selection strategies. The approach also demonstrates a notable reduction in the bias towards the "support" class observed in several models, leading to more balanced and robust predictions across different argument stances.

Key takeaway

For NLP Engineers developing argument mining systems, if you are struggling with LLM prompt sensitivity or biased stance predictions, consider implementing Topic-Guided prompting. This method dynamically injects topic-specific context, demonstrably outperforming standard few-shot approaches and reducing "support" class bias. Your systems will achieve more balanced and robust argument stance classifications, improving the reliability of public discourse analysis.

Key insights

Dynamically integrating topic-specific information into LLM prompts improves argument stance classification accuracy and reduces bias.

Principles

• LLM prompt sensitivity impacts few-shot performance.
• Topic-specific context enhances stance classification.
• Bias reduction improves prediction robustness.

Method

The method dynamically integrates topic-specific information into the few-shot context of Large Language Models to guide argument stance classification.

In practice

• Apply topic-guided prompting for LLM stance tasks.
• Use topic context to balance stance predictions.
• Evaluate against standard few-shot methods.

Topics

• Argument Mining
• Stance Classification
• Large Language Models
• Prompt Engineering
• Few-Shot Learning
• Bias Reduction

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.