TASE: Token Awareness and Structured Evaluation for Multilingual Language Models

• URL: http://arxiv.org/abs/2508.05468v1
• Date: Thu, 07 Aug 2025 15:11:17 GMT
• Title: TASE: Token Awareness and Structured Evaluation for Multilingual Language Models
• Authors: Chenzhuo Zhao, Xinda Wang, Yue Huang, Junting Lu, Ziqian Liu,
• Abstract summary: TASE is a benchmark designed to evaluate large language models' ability to perceive and reason about token-level information.<n> TASE covers 10 tasks under two core categories: token awareness and structural understanding, spanning Chinese, English, and Korean.<n>We evaluate over 30 leading commercial and open-source LLMs, including O3, Claude 4, Gemini 2.5 Pro, and DeepSeek-R1.
• Score: 8.058965963418785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While large language models (LLMs) have demonstrated remarkable performance on high-level semantic tasks, they often struggle with fine-grained, token-level understanding and structural reasoning--capabilities that are essential for applications requiring precision and control. We introduce TASE, a comprehensive benchmark designed to evaluate LLMs' ability to perceive and reason about token-level information across languages. TASE covers 10 tasks under two core categories: token awareness and structural understanding, spanning Chinese, English, and Korean, with a 35,927-instance evaluation set and a scalable synthetic data generation pipeline for training. Tasks include character counting, token alignment, syntactic structure parsing, and length constraint satisfaction. We evaluate over 30 leading commercial and open-source LLMs, including O3, Claude 4, Gemini 2.5 Pro, and DeepSeek-R1, and train a custom Qwen2.5-14B model using the GRPO training method. Results show that human performance significantly outpaces current LLMs, revealing persistent weaknesses in token-level reasoning. TASE sheds light on these limitations and provides a new diagnostic lens for future improvements in low-level language understanding and cross-lingual generalization. Our code and dataset are publicly available at https://github.com/cyzcz/Tase .

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