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Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents A Triadic Suffix Tokenization Scheme for Numerical Reasoning Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? 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Multilingual Tokenization through the Lens of Indian Languages: Challenges and Insights
N J Karthika, Maharaj Brahma, Rohit Saluja, Ganesh Ramakrishnan, · 2025-06-22 · via cs.CL updates on arXiv.org

Tokenization plays a pivotal role in multilingual NLP. However, existing tokenizers are often skewed towards high-resource languages, limiting their effectiveness for linguistically diverse and morphologically rich languages such as those in the Indian subcontinent. This paper presents a comprehensive intrinsic evaluation of tokenization strategies across 17 Indian languages. We quantify the trade-offs between bottom-up and top-down tokenizer algorithms (BPE and Unigram LM), effects of vocabulary sizes, and compare strategies of multilingual vocabulary construction such as joint and cluster-based training. We also show that extremely low-resource languages can benefit from tokenizers trained on related high-resource languages. Our study provides practical insights for building more fair, efficient, and linguistically informed tokenizers for multilingual NLP.

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