Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages

• URL: http://arxiv.org/abs/2512.02841v1
• Date: Tue, 02 Dec 2025 14:54:54 GMT
• Title: Cross-Lingual Prompt Steerability: Towards Accurate and Robust LLM Behavior across Languages
• Authors: Lechen Zhang, Yusheng Zhou, Tolga Ergen, Lajanugen Logeswaran, Moontae Lee, David Jurgens,
• Abstract summary: System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time.<n>This paper presents a comprehensive study of how different system prompts steer models toward accurate and robust cross-lingual behavior.
• Score: 61.18573330164572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: System prompts provide a lightweight yet powerful mechanism for conditioning large language models (LLMs) at inference time. While prior work has focused on English-only settings, real-world deployments benefit from having a single prompt to operate reliably across languages. This paper presents a comprehensive study of how different system prompts steer models toward accurate and robust cross-lingual behavior. We propose a unified four-dimensional evaluation framework to assess system prompts in multilingual environments. Through large-scale experiments on five languages, three LLMs, and three benchmarks, we uncover that certain prompt components, such as CoT, emotion, and scenario, correlate with robust multilingual behavior. We develop a prompt optimization framework for multilingual settings and show it can automatically discover prompts that improve all metrics by 5-10%. Finally, we analyze over 10 million reasoning units and find that more performant system prompts induce more structured and consistent reasoning patterns, while reducing unnecessary language-switching. Together, we highlight system prompt optimization as a scalable path to accurate and robust multilingual LLM behavior.

Related papers

• An Empirical Study on the Effects of System Prompts in Instruction-Tuned Models for Code Generation [4.76360912129794]
  We systematically evaluate how system prompts affect code assistant.<n>We find that increasing system-prompt constraint specificity does not monotonically improve correctness.<n>For larger code-specialized models, few-shot examples can degrade performance relative to zero-shot generation.
  arXiv  Detail & Related papers  (2026-02-16T22:11:21Z)
• PolyPrompt: Automating Knowledge Extraction from Multilingual Language Models with Dynamic Prompt Generation [0.0]
  We introduce PolyPrompt, a novel, parameter-efficient framework for enhancing the multilingual capabilities of large language models (LLMs)<n>Our method learns a set of trigger tokens for each language through a gradient-based search, identifying the input query's language and selecting the corresponding trigger tokens which are prepended to the prompt during inference.<n>We perform experiments on two 1 billion parameter models, with evaluations on the global MMLU benchmark across fifteen typologically and resource diverse languages, demonstrating accuracy gains of 3.7%-19.9% compared to naive and translation-pipeline baselines.
  arXiv  Detail & Related papers  (2025-02-27T04:41:22Z)
• LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models [89.13128402847943]
  We present LUSIFER, a novel zero-shot approach that adapts LLM-based embedding models for multilingual tasks without requiring multilingual supervision.<n>LUSIFER's architecture combines a multilingual encoder, serving as a language-universal learner, with an LLM-based embedding model optimized for embedding-specific tasks.<n>We introduce a new benchmark encompassing 5 primary embedding tasks, 123 diverse datasets, and coverage across 14 languages.
  arXiv  Detail & Related papers  (2025-01-01T15:43:07Z)
• Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Following [51.18383180774354]
  We introduce Multi-IF, a new benchmark designed to assess Large Language Models' proficiency in following multi-turn and multilingual instructions. Our evaluation of 14 state-of-the-art LLMs on Multi-IF reveals that it presents a significantly more challenging task than existing benchmarks. languages with non-Latin scripts (Hindi, Russian, and Chinese) generally exhibit higher error rates, suggesting potential limitations in the models' multilingual capabilities.
  arXiv  Detail & Related papers  (2024-10-21T00:59:47Z)
• Parrot: Multilingual Visual Instruction Tuning [66.65963606552839]
  Existing methods typically align vision encoders with Multimodal Large Language Models (MLLMs) via supervised fine-tuning (SFT)<n>We propose PARROT, a novel approach that leverages textual guidance for visual token alignment at the language level.<n>We introduce the Massive Multilingual Multimodal Benchmark (MMMB), a new benchmark comprising 6 languages, 15 categories, and 12,000 questions.
  arXiv  Detail & Related papers  (2024-06-04T17:56:28Z)
• DIALIGHT: Lightweight Multilingual Development and Evaluation of Task-Oriented Dialogue Systems with Large Language Models [76.79929883963275]
  DIALIGHT is a toolkit for developing and evaluating multilingual Task-Oriented Dialogue (ToD) systems. It features a secure, user-friendly web interface for fine-grained human evaluation at both local utterance level and global dialogue level. Our evaluations reveal that while PLM fine-tuning leads to higher accuracy and coherence, LLM-based systems excel in producing diverse and likeable responses.
  arXiv  Detail & Related papers  (2024-01-04T11:27:48Z)
• Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt [98.26682501616024]
  We propose a novel model that uses a unified prompt for all languages, called UniPrompt. The unified prompt is computation by a multilingual PLM to produce language-independent representation. Our proposed methods can significantly outperform the strong baselines across different languages.
  arXiv  Detail & Related papers  (2022-02-23T11:57:52Z)

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.