PolyPrompt: Automating Knowledge Extraction from Multilingual Language Models with Dynamic Prompt Generation

• URL: http://arxiv.org/abs/2502.19756v1
• Date: Thu, 27 Feb 2025 04:41:22 GMT
• Title: PolyPrompt: Automating Knowledge Extraction from Multilingual Language Models with Dynamic Prompt Generation
• Authors: Nathan Roll,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) showcase increasingly impressive English benchmark scores, however their performance profiles remain inconsistent across multilingual settings. To address this gap, we introduce PolyPrompt, a novel, parameter-efficient framework for enhancing the multilingual capabilities of LLMs. 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. 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.

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