Jiaming Leng, Yunying Bi University of Science and Technology of China, Chuan Qin Chinese Academy of Sciences, Zhenya Huang University of Science and Technology of China, Bing Yin iFLYTEK, Haojie Ren, Yanyong Zhang University of Science and Technology of China, Chao Wang University of Science and Technology of China
Urban transportation systems require precise modeling of dynamic spatiotemporal patterns across diverse tasks, such as traffic forecasting, electric vehicle (EV) charging demand prediction, and taxi dispatch. Existing approaches suffer from two key limitations: traditional deep learning models are task-specific and lack generalization capabilities, whereas Large Language Models (LLMs) struggle with structured spatiotemporal data and numerical reasoning. To bridge this gap, we propose TransLLM, a unified multi-task framework that synergizes spatiotemporal encoding with LLM reasoning through learnable prompt composition. To enable LLMs to perceive complex graph dependencies, we design a noise-augmented spatiotemporal encoder that projects structured signals into the LLM’s embedding space. Furthermore, to overcome the rigidity of fixed prompt templates in heterogeneous traffic scenarios, we introduce an instance-level prompt routing mechanism trained via reinforcement learning. The framework operates by encoding spatiotemporal patterns into contextual representations, dynamically composing personalized prompts to guide LLM reasoning, and projecting the resulting representations through specialized output layers to generate task-specific predictions. Experiments on seven datasets and three tasks demonstrate that TransLLM outperforms many baselines, showing superior adaptability in both supervised and zero-shot settings with excellent generalization and robustness. Our code and data are available at https://github.com/lengjiaming/TransLLM.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Ning Li, Zheng Zhang, Zhenya Huang University of Science and Technology of China, Rui Li, Yi Zhan, Yinbo Luo, Qi Liu University of Science and Technology of China, Enhong Chen University of Science and Technology of China
The rapid advancement of large language models (LLMs) has driven the deployment of LLM-based AI tutors on online learning platforms. This widespread adoption highlights an urgent need for systematic benchmarks to evaluate their tutoring capabilities. However, existing evaluations predominantly focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning. To bridge this gap, we introduce LongTutor, a benchmark for long-term personalized tutoring grounded in formative assessment theory. Built from expert-annotated real-world learning logs, LongTutor evaluates LLMs across three progressive tasks: historical evidence acquisition, knowledge state diagnosis, and adaptive teaching action. Our experiments reveal a critical capability mismatch: while LLMs excel at evidence acquisition, they struggle to effectively leverage long-term history for accurate diagnosis and adaptive teaching. To enable scalable benchmark expansion, we further propose an automated generator–verifier pipeline, paving the way toward truly long-term AI tutoring systems.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zheng Zhang, Qi Liu University of Science and Technology of China, Siyuan Liang Nanyang Technological University, Ning Li, Zirui Hu Nanyang Technological University, Weibo Gao University of Science and Technology of China, Rui Li, Zhenya Huang University of Science and Technology of China 等 11 位作者
Large language models (LLMs) have achieved remarkable performance across diverse tasks, largely driven by large-scale pretraining. However, this data abundance introduces test data contamination, where benchmark datasets overlap with pretraining corpora, undermining the reliability of model evaluation by confounding memorization with genuine generalization. To mitigate this issue, existing training data detectors attempt to identify clean (unseen) samples from contaminated test sets, but often suffer from residual contamination due to the black-box nature of LLMs. As a result, contaminated data may be mistakenly retained, leading to unreliable evaluation.To address this challenge, we propose FTD (FDR-controlled Training Data detection), a principled framework that detects and filters contaminated evaluation data while providing a statistical guarantee: the proportion of contaminated samples mistakenly retained as clean, the false discovery rate (FDR), is provably controlled below a user-specified threshold. FTD combines multiple complementary detectors via an adaptive weighting strategy, and we theoretically show it achieves high statistical power under valid FDR control. Extensive experiments on real-world benchmarks demonstrate that FTD significantly reduces residual contamination compared to existing methods while preserving evaluation consistency.
尚无 AI 解读，展示原文摘要
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表