Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection

2026-07-21 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

A novel rationale-guided knowledge distillation framework is proposed to enhance cross-lingual stance detection, particularly for low-resource languages like Catalan. This method addresses the limitations of existing approaches that primarily rely on semantic alignment and the high computational cost of Large Language Models (LLMs). The framework utilizes Chain-of-Thought prompting to guide LLMs in generating informative rationales, subsequently distilling this reasoning knowledge into a compact student model. It further incorporates a dual-path distillation mechanism to align both rationale-enhanced and rationale-free representations, alongside their prediction distributions. Additionally, two contrastive learning strategies are introduced to improve stance discrimination. Experiments on multilingual benchmarks demonstrate that this method consistently outperforms competitive baselines, offering a more efficient solution for transferring stance knowledge.

Key takeaway

For NLP Engineers developing cross-lingual stance detection systems in low-resource languages, consider implementing rationale-guided knowledge distillation. This approach allows you to leverage powerful Large Language Model reasoning without incurring high computational costs, significantly improving model performance over semantic-only methods. Focus on generating high-quality rationales via Chain-of-Thought prompting and integrating dual-path distillation for robust knowledge transfer, ensuring your models are both effective and deployable.

Key insights

Distilling LLM-generated rationales into compact models improves cross-lingual stance detection for low-resource languages.

Principles

• Cross-lingual stance detection benefits from explicit reasoning processes.
• Knowledge distillation can transfer LLM reasoning to smaller models.
• Dual-path distillation aligns diverse representation types effectively.

Method

Uses Chain-of-Thought prompting with LLMs to generate rationales, distills this reasoning into a compact student model via a dual-path mechanism, and employs contrastive learning for stance discrimination.

In practice

• Apply Chain-of-Thought prompting to LLMs for rationale generation.
• Implement dual-path distillation for representation alignment.
• Utilize contrastive learning to boost stance discrimination.

Topics

• Stance Detection
• Cross-Lingual NLP
• Knowledge Distillation
• Large Language Models
• Chain-of-Thought
• Contrastive Learning

Best for: Research Scientist, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.