Multilingual Prompting for Improving LLM Generation Diversity

• URL: http://arxiv.org/abs/2505.15229v1
• Date: Wed, 21 May 2025 07:59:21 GMT
• Title: Multilingual Prompting for Improving LLM Generation Diversity
• Authors: Qihan Wang, Shidong Pan, Tal Linzen, Emily Black,
• Abstract summary: Large Language Models (LLMs) are known to lack cultural representation and overall diversity in their generations.<n>We propose multilingual prompting: a prompting method which generates several variations of a base prompt with added cultural and linguistic cues from several cultures.
• Score: 21.092757338375037
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) are known to lack cultural representation and overall diversity in their generations, from expressing opinions to answering factual questions. To mitigate this problem, we propose multilingual prompting: a prompting method which generates several variations of a base prompt with added cultural and linguistic cues from several cultures, generates responses, and then combines the results. Building on evidence that LLMs have language-specific knowledge, multilingual prompting seeks to increase diversity by activating a broader range of cultural knowledge embedded in model training data. Through experiments across multiple models (GPT-4o, GPT-4o-mini, LLaMA 70B, and LLaMA 8B), we show that multilingual prompting consistently outperforms existing diversity-enhancing techniques such as high-temperature sampling, step-by-step recall, and personas prompting. Further analyses show that the benefits of multilingual prompting vary with language resource level and model size, and that aligning the prompting language with the cultural cues reduces hallucination about culturally-specific information.

Related papers

• Disentangling Language and Culture for Evaluating Multilingual Large Language Models [48.06219053598005]
  This paper introduces a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of LLMs.<n>By decomposing the evaluation along the dimensions of linguistic medium and cultural context, this framework enables a nuanced analysis of LLMs' ability to process questions cross-lingually.
  arXiv  Detail & Related papers  (2025-05-30T14:25:45Z)
• MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs [26.806566827956875]
  MAKIEval is an automatic multilingual framework for evaluating cultural awareness in large language models.<n>It automatically identifies cultural entities in model outputs and links them to structured knowledge.<n>We assess 7 LLMs developed from different parts of the world, encompassing both open-source and proprietary systems.
  arXiv  Detail & Related papers  (2025-05-27T19:29:40Z)
• All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages [73.93600813999306]
  ALM-bench is the largest and most comprehensive effort to date for evaluating LMMs across 100 languages.<n>It challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages.<n>The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions.
  arXiv  Detail & Related papers  (2024-11-25T15:44:42Z)
• Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages [55.36534539177367]
  This paper introduces Pangea, a multilingual multimodal large language model (MLLM) trained on a diverse 6M instruction dataset spanning 39 languages.<n>P Pangea significantly outperforms existing open-source models in multilingual settings and diverse cultural contexts.<n>We fully open-source our data, code, and trained checkpoints, to facilitate the development of inclusive and robust multilingual MLLMs.
  arXiv  Detail & Related papers  (2024-10-21T16:19:41Z)
• Teaching LLMs to Abstain across Languages via Multilingual Feedback [40.84205285309612]
  We show that multilingual feedback helps identify knowledge gaps across diverse languages, cultures, and communities.<n>Extensive experiments demonstrate that our multilingual feedback approach outperforms various strong baselines.<n>Further analysis reveals that multilingual feedback is both an effective and a more equitable abstain strategy to serve diverse language speakers.
  arXiv  Detail & Related papers  (2024-06-22T21:59:12Z)
• A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers [51.8203871494146]
  The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing.<n>Despite the breakthroughs of LLMs, the investigation into the multilingual scenario remains insufficient.<n>This survey aims to help the research community address multilingual problems and provide a comprehensive understanding of the core concepts, key techniques, and latest developments in multilingual natural language processing based on LLMs.
  arXiv  Detail & Related papers  (2024-05-17T17:47:39Z)
• 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)
• Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models [12.700783525558721]
  English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks. This paper introduces the decomposed prompting approach to probe the linguistic structure understanding of these LLMs in sequence labeling tasks.
  arXiv  Detail & Related papers  (2024-02-28T15:15:39Z)
• Investigating Cultural Alignment of Large Language Models [10.738300803676655]
  We show that Large Language Models (LLMs) genuinely encapsulate the diverse knowledge adopted by different cultures. We quantify cultural alignment by simulating sociological surveys, comparing model responses to those of actual survey participants as references. We introduce Anthropological Prompting, a novel method leveraging anthropological reasoning to enhance cultural alignment.
  arXiv  Detail & Related papers  (2024-02-20T18:47:28Z)
• Multi-lingual and Multi-cultural Figurative Language Understanding [69.47641938200817]
  Figurative language permeates human communication, but is relatively understudied in NLP. We create a dataset for seven diverse languages associated with a variety of cultures: Hindi, Indonesian, Javanese, Kannada, Sundanese, Swahili and Yoruba. Our dataset reveals that each language relies on cultural and regional concepts for figurative expressions, with the highest overlap between languages originating from the same region. All languages exhibit a significant deficiency compared to English, with variations in performance reflecting the availability of pre-training and fine-tuning data.
  arXiv  Detail & Related papers  (2023-05-25T15:30:31Z)

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.