Knowledge tracing (KT) aims to predict learners’ mastery levels of knowledge components (KCs) or test items based on their interaction records with educational content. Despite significant advancements in KT models, such as RNN-based sequence models and Transformer-based attention models, a critical limitation persists...
Mingliang Hou, Zitao Liu, Ren-Qiang Luo et al.· ACM Transactions on Informat...· 0 citations
A unified benchmark that evaluates representative machine learning, reinforcement learning, LLM-based, and agent-based trading methods in cryptocurrency markets through three progressively more realistic stages through historical backtesting, prospective exchange-based paper trading, and real-money live trading is pres...
Xing-Tong Yu, Jia-Run Zhou, Guan-Lin Ding et al.· 0 citations
Language models (LMs) often hallucinate by committing to confident answers rather than abstaining, even when they do not have enough information to answer reliably. A large body of existing work mitigates hallucination through detection or abstention mechanisms, but leaves open how models internally arrive at the decis...
Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al.· 0 citations
RBwA, a memory-efficient and sample-efficient progressive sampling algorithm for IM-PC and a memory-efficient rounding scheme called BwARound for coverage maximization subroutines, which only requires storing one fractional vector and takes maximal feasible steps rather than tiny ε-increments, are proposed.
Qixin Zhang, Qirun Zeng, Hui Lu et al.· Proceedings of the 32nd ACM...· 0 citations
This work delineates LLM reasoning boundaries and presents a new paradigm for fine-grained capability assessment, which suggests that genuine reasoning is demonstrated only when a model follows logical rules despite conflicting prior knowledge.
Fangfei Yan, Jianbo Yao, Michael K. Chen et al.· Proceedings of the 32nd ACM...· 1 citation
Twin Worlds (TW), a framework for improving reliability in knowledge-intensive reasoning through equivariance-based abstention, is proposed, which identifies when answers are not reliably grounded in the provided evidence and outperforms uncertainty- and sufficiency-based baselines.
Vy Nguyen, Zi-Qi Xu, Jeffrey Chan et al.· 1 citation
This work delineates LLM reasoning boundaries and presents a new paradigm for fine-grained capability assessment, which suggests that genuine reasoning is demonstrated only when a model follows logical rules despite conflicting prior knowledge.
Fangfei Yan, Jianbo Yao, Michael K. Chen et al.· Proceedings of the 32nd ACM...· 1 citation
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