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LLMs struggle with cultural nuances and cross-lingual transfer in sentiment analysis

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 9 sources

Two new papers explore the capabilities of large language models (LLMs) in understanding nuanced language across different cultures and languages. One study evaluates cross-lingual transfer strategies for aspect-based sentiment analysis, finding that fine-tuned LLMs perform best, especially when trained on multiple non-target languages. The other paper investigates whether LLMs grasp embodied cognition and cultural variations, concluding that current models fail to inherently understand cultural differences and default to English-centric reasoning. AI

Summary written by gemini-2.5-flash-lite from 9 sources. How we write summaries →

IMPACT Highlights limitations in current LLMs' cross-lingual and cultural understanding, suggesting areas for future model development.

RANK_REASON The cluster contains two academic papers published on arXiv, detailing research into LLM capabilities.

Read on arXiv cs.CL →

COVERAGE [9]

  1. arXiv cs.CL TIER_1 · Lisa Beinborn ·

    What makes a word hard to learn? Modeling L1 influence on English vocabulary difficulty

    What makes a word difficult to learn, and how does the difficulty depend on the learner's native language? We computationally model vocabulary difficulty for English learners whose first language is Spanish, German, or Chinese with gradient-boosted models trained on features rela…

  2. arXiv cs.CL TIER_1 · Debasmita Bhattacharya, Marten van Schijndel ·

    Code-switching in text and speech challenges information-theoretic speaker design

    arXiv:2408.04596v2 Announce Type: replace Abstract: In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker alternates between one language variety (the primary language) and another (the se…

  3. arXiv cs.LG TIER_1 · Gregory N. Frank ·

    Detection Is Cheap, Routing Is Learned: Why Refusal-Based Alignment Evaluation Fails

    arXiv:2603.18280v3 Announce Type: replace Abstract: Current alignment evaluation mostly measures whether models encode dangerous concepts and whether they refuse harmful requests. Both miss the layer where alignment often operates: routing from concept detection to behavioral pol…

  4. arXiv cs.CL TIER_1 · Jakob Fehle, Nils Constantin Hellwig, Udo Kruschwitz, Christian Wolff ·

    Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis

    arXiv:2604.26619v1 Announce Type: new Abstract: Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite major advances in transformer-based and instruction-tuned models. This work presen…

  5. arXiv cs.CL TIER_1 · Christian Wolff ·

    Zero-Shot to Full-Resource: Cross-lingual Transfer Strategies for Aspect-Based Sentiment Analysis

    Aspect-based Sentiment Analysis (ABSA) extracts fine-grained opinions toward specific aspects within text but remains largely English-focused despite major advances in transformer-based and instruction-tuned models. This work presents a multilingual evaluation of state-of-the-art…

  6. arXiv cs.CL TIER_1 · Yu Wang, Emmanuele Chersoni, Chu-Ren Huang ·

    Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives

    arXiv:2604.25423v1 Announce Type: new Abstract: Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "zh\"e/n\"a" in Chinese, as a novel pr…

  7. Hugging Face Daily Papers TIER_1 ·

    Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives

    Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "zhè/nà" in Chinese, as a novel probe for grounded knowledge. Using 6,400 responses fr…

  8. arXiv cs.CL TIER_1 · Chu-Ren Huang ·

    Do LLMs Capture Embodied Cognition and Cultural Variation? Cross-Linguistic Evidence from Demonstratives

    Do large language models (LLMs) truly acquire embodied cognition and cultural conventions from text? We introduce demonstratives, fundamental spatial expressions like "this/that" in English and "zhè/nà" in Chinese, as a novel probe for grounded knowledge. Using 6,400 responses fr…

  9. arXiv cs.CL TIER_1 · Lung-Hao Lee, Liang-Chih Yu, Natalia Loukashevich, Ilseyar Alimova, Alexander Panchenko, Tzu-Mi Lin, Zhe-Yu Xu, Jian-Yu Zhou, Guangmin Zheng, Jin Wang, Sharanya Awasthi, Jonas Becker, Jan Philip Wahle, Terry Ruas, Shamsuddeen Hassan Muhammad, Saif M. Moha ·

    DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis

    arXiv:2601.23022v3 Announce Type: replace Abstract: Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical la…

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