Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions

• URL: http://arxiv.org/abs/2602.05932v1
• Date: Thu, 05 Feb 2026 17:44:06 GMT
• Title: Polyglots or Multitudes? Multilingual LLM Answers to Value-laden Multiple-Choice Questions
• Authors: Léo Labat, Etienne Ollion, François Yvon,
• Abstract summary: Multiple-Choice Questions (MCQs) are often used to assess knowledge, reasoning abilities, and even values encoded in large language models (LLMs)
• Score: 16.64653069179642
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multiple-Choice Questions (MCQs) are often used to assess knowledge, reasoning abilities, and even values encoded in large language models (LLMs). While the effect of multilingualism has been studied on LLM factual recall, this paper seeks to investigate the less explored question of language-induced variation in value-laden MCQ responses. Are multilingual LLMs consistent in their responses across languages, i.e. behave like theoretical polyglots, or do they answer value-laden MCQs depending on the language of the question, like a multitude of monolingual models expressing different values through a single model? We release a new corpus, the Multilingual European Value Survey (MEVS), which, unlike prior work relying on machine translation or ad hoc prompts, solely comprises human-translated survey questions aligned in 8 European languages. We administer a subset of those questions to over thirty multilingual LLMs of various sizes, manufacturers and alignment-fine-tuning status under comprehensive, controlled prompt variations including answer order, symbol type, and tail character. Our results show that while larger, instruction-tuned models display higher overall consistency, the robustness of their responses varies greatly across questions, with certain MCQs eliciting total agreement within and across models while others leave LLM answers split. Language-specific behavior seems to arise in all consistent, instruction-fine-tuned models, but only on certain questions, warranting a further study of the selective effect of preference fine-tuning.

Related papers

• A method for improving multilingual quality and diversity of instruction fine-tuning datasets [29.07537849245622]
  We introduce Multilingual Data Quality and Diversity (M-DaQ) to improve Multilingual Instruction Fine-Tuning (IFT)<n>M-DaQ is a novel method for improving LLMs multilinguality by selecting high-quality and semantically diverse multilingual IFT samples.<n> Empirical results across 18 languages demonstrate that models fine-tuned with M-DaQ achieve significant performance gains over vanilla baselines over 60% win rate.
  arXiv  Detail & Related papers  (2025-09-19T03:07:59Z)
• CALM: Unleashing the Cross-Lingual Self-Aligning Ability of Language Model Question Answering [42.92810049636768]
  Large Language Models (LLMs) are pretrained on extensive multilingual corpora to acquire both language-specific cultural knowledge and general knowledge.<n>We explore the Cross-Lingual Self-Aligning ability of Language Models (CALM) to align knowledge across languages.<n>We employ direct preference optimization (DPO) to align the model's knowledge across different languages.
  arXiv  Detail & Related papers  (2025-01-30T16:15:38Z)
• 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)
• Multilingual Needle in a Haystack: Investigating Long-Context Behavior of Multilingual Large Language Models [22.859955360764275]
  We introduce the MultiLingual Needle-in-a-Haystack (MLNeedle) test to assess a model's ability to retrieve relevant information. We evaluate four state-of-the-art large language models on MLNeedle.
  arXiv  Detail & Related papers  (2024-08-19T17:02:06Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts.<n>We find that Llama Instruct and Mistral models exhibit high degrees of language confusion.<n>We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• CaLMQA: Exploring culturally specific long-form question answering across 23 languages [58.18984409715615]
  CaLMQA is a dataset of 51.7K culturally specific questions across 23 different languages.<n>We evaluate factuality, relevance and surface-level quality of LLM-generated long-form answers.
  arXiv  Detail & Related papers  (2024-06-25T17:45:26Z)
• MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models [65.10456412127405]
  MLaKE is a benchmark for the adaptability of knowledge editing methods across five languages.<n>MLaKE aggregates fact chains from Wikipedia across languages and generates questions in both free-form and multiple-choice.<n>We evaluate the multilingual knowledge editing generalization capabilities of existing methods on MLaKE.
  arXiv  Detail & Related papers  (2024-04-07T15:23:28Z)
• Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ [16.637598165238934]
  Large language models (LLMs) need to serve everyone, including a global majority of non-English speakers. Recent research shows that, despite limits in their intended use, people prompt LLMs in many different languages. We introduce MultiQ, a new silver standard benchmark for basic open-ended question answering with 27.4k test questions.
  arXiv  Detail & Related papers  (2024-03-06T16:01:44Z)
• How do Large Language Models Handle Multilingualism? [81.15060972112563]
  This study explores how large language models (LLMs) handle multilingualism. LLMs initially understand the query, converting multilingual inputs into English for task-solving. In the intermediate layers, they employ English for thinking and incorporate multilingual knowledge with self-attention and feed-forward structures.
  arXiv  Detail & Related papers  (2024-02-29T02:55:26Z)
• On the Calibration of Multilingual Question Answering LLMs [57.296161186129545]
  We benchmark the calibration of several multilingual Large Language Models (MLLMs) on a variety of Question Answering tasks. We study different dimensions of calibration in in-distribution, out-of-distribution, and cross-lingual transfer settings. For decoder-only LLMs such as LlaMa2, we additionally find that in-context learning improves confidence calibration on multilingual data.
  arXiv  Detail & Related papers  (2023-11-15T03:29:02Z)
• Applying Multilingual Models to Question Answering (QA) [0.0]
  We study the performance of monolingual and multilingual language models on the task of question-answering (QA) on three diverse languages: English, Finnish and Japanese. We develop models for the tasks of (1) determining if a question is answerable given the context and (2) identifying the answer texts within the context using IOB tagging.
  arXiv  Detail & Related papers  (2022-12-04T21:58:33Z)
• Delving Deeper into Cross-lingual Visual Question Answering [115.16614806717341]
  We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers.
  arXiv  Detail & Related papers  (2022-02-15T18:22:18Z)
• Multilingual Answer Sentence Reranking via Automatically Translated Data [97.98885151955467]
  We present a study on the design of multilingual Answer Sentence Selection (AS2) models, which are a core component of modern Question Answering (QA) systems. The main idea is to transfer data, created from one resource rich language, e.g., English, to other languages, less rich in terms of resources.
  arXiv  Detail & Related papers  (2021-02-20T03:52:08Z)

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.