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Source note2 min read

Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection

Source

arXiv

arxiv.org/abs/2607.18693v1

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Source

Plain-Language Brief

The authors propose a method called rationale-guided knowledge distillation for cross-lingual stance detection. This technique aims to identify attitudes in texts towards a target, and helps when there is a lack of annotated data in low-resource languages. The method uses Large Language Models (LLMs) that have strong reasoning abilities but high computational cost and latency. To address these limitations, the authors guide LLMs to generate informative rationales using Chain-of-Thought prompting, then distill the reasoning knowledge into a compact student model. They also introduce dual-path distillation and contrastive learning strategies to enhance performance.

What the Document Says

  • Stance detection aims to identify attitudes in texts towards a target
  • Existing methods for cross-lingual stance detection rely on semantic alignment between texts and targets
  • Large Language Models provide strong reasoning ability but have high computational cost and latency
  • Proposed method uses Chain-of-Thought prompting to guide LLMs in generating informative rationales
  • Distilled reasoning knowledge is used to create a compact student model
  • Dual-path distillation mechanism aligns rationale-enhanced and rationale-free representations
  • Contrastive learning strategies are introduced for improved stance discrimination
  • Experiments on multilingual benchmarks demonstrate the method's consistent outperformance of competitive baselines.

Key Concepts Mentioned

  • Stance detection
  • Cross-lingual stance detection
  • Large Language Models
  • Rationale-guided knowledge distillation
  • Chain-of-Thought prompting
  • Dual-path distillation
  • Contrastive learning

Claims Made by the Document

  • Our method outperforms competitive baselines in cross-lingual stance detection.

Evidence Used

  • Experiments on multilingual benchmarks demonstrate that our method consistently outperforms competitive baselines.

Methods / Approach

  • Rationale-guided knowledge distillation
  • Chain-of-Thought prompting
  • Dual-path distillation
  • Contrastive learning

Limitations Stated by the Authors

Not stated in source

Limitations Noted by the Agent

Agent interpretation — not from the source.

The method might not generalize well to all languages or stance detection scenarios outside of the tested benchmarks.

Open Questions

  • How well does the proposed method perform in other languages and stance detection scenarios beyond the tested benchmarks?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The authors do not provide information on how they defined 'competitive baselines' or the specific languages included in their multilingual benchmarks.

Raw Abstract / Excerpt

Stance detection aims to identify whether a text expresses a favorable or opposing attitude toward a given target, and serves as an important task for various downstream applications. Although existing studies have achieved strong performance in monolingual settings, especially in English, many low-resource languages such as Catalan still lack sufficient annotated data for training effective models. Cross-lingual stance detection alleviates this problem by transferring stance knowledge from resource-rich languages to low-resource languages. However, most existing methods mainly rely on semantic alignment between texts and targets, while ignoring the reasoning process required for reliable stance inference. Although Large Language Models provide strong reasoning ability, their high computational cost and inference latency limit practical deployment. To address these limitations, we propose a rationale-guided knowledge distillation framework for cross-lingual stance detection. Specifically, we use Chain-of-Thought prompting to guide Large Language Models in generating informative rationales, and distill the resulting reasoning knowledge into a compact student model. We further design a dual-path distillation mechanism to align rationale-enhanced and rationale-free representations, together with their prediction distributions. In addition, two contrastive learning strategies are introduced to improve stance discrimination. Experiments on multilingual benchmarks demonstrate that our method consistently outperforms competitive baselines.