TRUEBench: Can LLM Response Meet Real-world Constraints as Productivity Assistant? (2025.findings-emnlp)
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| Challenge: | Existing benchmarks fail to evaluate large language models' instruction-following capabilities . current benchmarks lack multilinguality, implicit constraints and multi-turn dialogue . |
| Approach: | a new benchmark is designed to evaluate large language models' instruction-following capabilities . the benchmark features input prompts across 12 languages and includes inter-instance multilingual instructions . |
| Outcome: | a new benchmark for large language models (LLMs) is designed to assess their performance in real-world settings. |
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Tao Zhang, ChengLIn Zhu, Yanjun Shen, Wenjing Luo, Yan Zhang, Hao Liang, Tao Zhang, Fan Yang, Mingan Lin, Yujing Qiao, Weipeng Chen, Bin Cui, Wentao Zhang, Zenan Zhou
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ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)
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Chen Chen, Xinlong Hao, Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Yuefeng Huang, Xiangcheng Liu, Wang Xinzhi, Wu Liu
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| Challenge: | Recent advances in large language models (LLMs) have enabled real-time speech interactions through LLMs. |
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| Challenge: | Existing evaluation benchmarks for Large Language Models focus on objective tasks like mathematics and coding in English, which do not reflect the practical use cases of on-device LLMs in real-world mobile scenarios. |
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| Challenge: | Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. |
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| Challenge: | Existing benchmarks focus on single-turn or single-step tasks, failing to capture iterative reasoning in real-world settings. |
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| Challenge: | Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries. |
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Metric Calculating Benchmark: Code-Verifiable Complicate Instruction Following Benchmark for Large Language Models (2025.emnlp-main)
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| Challenge: | Recent frontier-level LLMs have saturated many previously difficult benchmarks, leaving little room for further differentiation. |
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UniToolBench: A Benchmark for Tool-Augmented LLMs in Cross-Domain, Universal Task Automation (2026.findings-eacl)
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| Challenge: | Existing benchmarks that focus on manually curated tool graphs lack scalability and diversity across domains. |
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