Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts

2026-03-17

AI Summary

This study addresses a critical bottleneck in cross-lingual parametric knowledge transfer for large reasoning language models: script mismatch, rather than linguistic family or language divergence, is identified as the primary barrier. Through analysis of the ECLeKTic and MultiLoKo datasets, the work reveals that disparities in writing systems significantly impede knowledge generalization across languages. To mitigate this, the authors propose enhancing the model’s capacity to handle transliteration ambiguity during inference, complemented by regression-based analysis, entity back-translation prompting, synthetic data generation, and targeted supervised fine-tuning (SFT). Experimental results demonstrate that this integrated approach effectively narrows the knowledge transfer gap in cross-script scenarios, establishing the feasibility of improving cross-lingual parametric knowledge transfer through post-training interventions.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this work, we analyze shortcomings in cross-lingual knowledge transfer in large, modern reasoning LLMs. We demonstrate that the perceived gap in knowledge transfer is primarily a script barrier. First, we conduct an observational data analysis on the performance of thinking models on two datasets with local knowledge from around the world, ECLeKTic and MultiLoKo. Our regression analysis shows that script match - not language or family - is the primary predictor of knowledge transfer failure once model capability and question difficulty are accounted for. We further this finding by providing the LLMs with the key entities of the questions in their source language and find that this disproportionately improves cross-script questions. We then posit that these LLMs could be reasoning better at test-time. To evaluate this, we develop a synthetic generation pipeline to design SFT samples to encourage the model to better reason about transliteration ambiguities when trying to fetch parametric knowledge at inference-time. We show that teaching two models to reason better reduces the cross-script transfer gap. As a result, we conclude that there is potential to improve cross-lingual parametric knowledge transfer during post-training.

Problem

• cross-lingual knowledge transfer
• script barrier
• parametric knowledge
• large reasoning models
• transliteration ambiguity

Innovation

• cross-script transfer
• parametric knowledge
• reasoning LLMs
• transliteration ambiguity
• synthetic SFT