Papers by Maolin Ma

2 papers
Learning from Near-Misses: Error-Aware Contrastive Few-Shot Learning for NL2Formula (2026.acl-long)

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Challenge: Existing spreadsheet formulas often produce near-miss outputs due to an incorrect function, operator, or reference.
Approach: They propose an abstract syntax tree-based error taxonomy that organizes common error modes by the kind of decision that goes wrong in the parse tree.
Outcome: The proposed framework improves Exact Match (EM) by 6.4 points over supervised fine-tuning and matches self-consistency (SC@5) accuracy.
MTA:A Merge-then-Adapt Framework for Personalized Large Language Models (2026.acl-long)

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Challenge: Personalized Large Language Models (PLLMs) aim to align outputs with individual user preferences . current methods of fine-tuning a separate module for each user are unscalable .
Approach: They propose a Merge-then-Adapt framework for Personalized Large Language Models . they construct a shared Meta-LoRA bank and propose an Adaptive LoRA Fusion stage .
Outcome: The proposed framework outperforms existing SOTA methods on the LaMP benchmark.

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