Unlocking LLM Safeguards for Low-Resource Languages via Reasoning and Alignment with Minimal Training Data

• URL: http://arxiv.org/abs/2510.10677v1
• Date: Sun, 12 Oct 2025 16:05:56 GMT
• Title: Unlocking LLM Safeguards for Low-Resource Languages via Reasoning and Alignment with Minimal Training Data
• Authors: Zhuowei Chen, Bowei Zhang, Nankai Lin, Tian Hou, Lianxi Wang,
• Abstract summary: We propose a novel reasoning-based multilingual safeguard that enhances explainability via reasoning and boosts knowledge transfer between languages through alignment.<n>With only 1,000 training samples, our method demonstrates superior performance on three datasets across six languages, outperforming larger models trained with significantly more data, and exhibits strong interpretability and generalization ability.
• Score: 9.30969710371555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in LLMs have enhanced AI capabilities, but also increased the risk posed by malicious requests, highlighting the need for effective LLM safeguards to detect such queries. Existing approaches largely rely on classifier-based methods that lack interpretability and perform poorly on low-resource languages. To address these limitations, we propose ConsistentGuard, a novel reasoning-based multilingual safeguard, which enhances explainability via reasoning and boosts knowledge transfer between languages through alignment. With only 1,000 training samples, our method demonstrates superior performance on three datasets across six languages, outperforming larger models trained with significantly more data, and exhibits strong interpretability and generalization ability. We also contribute a multilingual benchmark extension and release our codes to support future research.

Related papers

• Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering [44.596434537567916]
  We propose a training-free inference-time method to enhance Multilingual Reasoning capabilities via Representation Engineering (MRRE)<n>MRRE consistently enhances non-English reasoning by an average gain of 5.48% and up to 7.54% in low-resource languages (Thai and Swahili)
  arXiv  Detail & Related papers  (2025-11-28T14:40:27Z)
• Code-Switching In-Context Learning for Cross-Lingual Transfer of Large Language Models [64.54005959758733]
  We introduce code-switching in-context learning (CSICL) as a principled and robust approach for overcoming the translation barrier during inference.<n>We conduct extensive experiments across 4 LLMs, 6 datasets, and 10 languages, spanning both knowledge-intensive and reasoning-oriented domains.<n>Our results demonstrate CSICL consistently outperforms X-ICL baselines, achieving gains of 3.1%p and 1.9%p in both target and unseen languages.
  arXiv  Detail & Related papers  (2025-10-07T08:35:42Z)
• Large Language Models for Multilingual Vulnerability Detection: How Far Are We? [13.269680075539135]
  We evaluate the effectiveness of pre-trained language models (PLMs) and large language models (LLMs) for multilingual vulnerability detection.<n>Using over 30,000 real-world vulnerability-fixing patches across seven programming languages, we assess model performance at both the function-level and line-level.<n>Our key findings indicate that GPT-4o, enhanced through instruction tuning and few-shot prompting, significantly outperforms all other evaluated models.
  arXiv  Detail & Related papers  (2025-06-09T07:27:49Z)
• A Preliminary Study of Large Language Models for Multilingual Vulnerability Detection [13.269680075539135]
  Large language models (LLMs) offer language-agnostic capabilities and enhanced semantic understanding.<n>Recent advancements in large language models (LLMs) offer language-agnostic capabilities and enhanced semantic understanding.<n>Our findings reveal that the PLM CodeT5P achieves the best performance in multilingual vulnerability detection.
  arXiv  Detail & Related papers  (2025-05-12T09:19:31Z)
• Is Small Language Model the Silver Bullet to Low-Resource Languages Machine Translation? [18.975160361440597]
  Low-resource languages (LRLs) lack sufficient linguistic resources and are underrepresented in benchmark datasets.<n>This study systematically evaluates state-of-the-art smaller Large Language Models in 200 languages.
  arXiv  Detail & Related papers  (2025-03-31T13:56:03Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization [108.6908427615402]
  Cross-lingual summarization ( CLS) aims to generate a summary for the source text in a different target language.<n>Currently, instruction-tuned large language models (LLMs) excel at various English tasks.<n>Recent studies have shown that LLMs' performance on CLS tasks remains unsatisfactory even with few-shot settings.
  arXiv  Detail & Related papers  (2024-10-26T00:39:44Z)
• The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts [46.089025223336854]
  This paper examines the variations in safety challenges faced by large language models across different languages. We compare how state-of-the-art LLMs respond to the same set of malicious prompts written in higher- vs. lower-resource languages.
  arXiv  Detail & Related papers  (2024-01-23T23:12:09Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.