Yefan Zeng, Shengyu Duan, Rishad Shafik, Alex Yakovlev
The Tsetlin Machine (TM) is a logic-based machine learning model well suited to low-latency inference on resource-constrained edge devices. Because each clause is an AND over Boolean literals, both the literals and their associated Tsetlin Automaton (TA) actions can be represented at the bit level, enabling bitwise evaluation and an early exit that is triggered as soon as any included literal evaluates to zero, forcing the clause output to zero. In this work, we study how the two TM training hyperparameters, T and s, govern both classification accuracy and inference latency. Building on the inversely proportional T-s relationship along which accuracy is nearly constant, we find that inference latency is reduced primarily by increasing s; rather than exhaustively searching the (T, s) grid at high training cost, we propose a directed search strategy that treats accuracy as a constraint and advances toward larger s to reduce inference latency within a fixed evaluation budget. Experimental results on the gem5 simulator with an ARM processor show that, for certain datasets, the search is able to increase s while preserving the baseline accuracy, thereby reducing inference latency at negligible accuracy cost: across six datasets, the method reduces inference latency by up to 16.34%, while some datasets exhibit an accuracy reduction of only 0.06%. The proposed strategy requires far fewer configuration evaluations than an exhaustive grid search.
Zhihao Shuai, Yiyun Chen The Hong Kong University of Science and Technology, Maolin Ma, Yutong Chen, Hanjia Qiu, Jing Xu The Hong Kong University of Science and Technology (Guanzhou), Ziye Chen, Weikai Yang Hong Kong University of Science and Technology (Guangzhou)
Natural Language to Excel Formula (NL2Formula) translates user intent into executable spreadsheet formulas. However, current models often produce near-miss outputs—formulas that parse correctly yet fail at execution due to an incorrect function, operator, or reference. Through a systematic error analysis, we find that these errors repeatedly arise from a small set of structural decision points, motivating the need for typed error supervision rather than general error signals. To this end, we introduce an abstract syntax tree (AST)-based error taxonomy that organizes common error modes by the kind of decision that goes wrong in the parse tree. Building on this taxonomy, we propose Error-Aware Contrastive Few-Shot Learning (ECFL), an error-aware framework that unifies training and inference around typed error supervision. During offline training, ECFL mines near-miss errors, assigns error types under the taxonomy, and builds error-aware contrastive demonstrations for fine-tuning. During online inference, a lightweight predictor estimates likely error types and triggers targeted retrieval of contrastive demonstrations to guide single-pass decoding. Experiments show ECFL improves Exact Match (EM) by 6.4 points over supervised fine-tuning (SFT) and matches self-consistency (SC@5) accuracy at substantially lower inference cost.
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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