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新论文 · v1arXiv:2611.02637

Variable names provide priors: semantic grounding enables data-efficient numerical prediction by large language models

Xin-Rui Ma, Shi-Zhi Chen, Hai-Yan Jiang, De-Cheng Feng, Michael Beer

原文摘要

Large language models (LLMs) have achieved strong performance across diverse tasks, from question answering to code generation. Recent studies have found that LLMs can perform numerical prediction on tabular datasets via in-context learning. However, it remains unclear under what conditions and how LLMs use variable names and in-context examples for numerical prediction. Here we show that semantic grounding, the match between variable names and the values they describe, allows five LLMs without task-specific training to match or outperform seven supervised machine learning (ML) models across 20 datasets from nine scientific domains, particularly when examples are scarce. Reasoning traces indicate that variable names allow LLMs to draw on domain-specific knowledge acquired during pretraining. Using controlled tasks based on physical formulas, we further show that LLMs integrate domain-specific knowledge with in-context examples in a way consistent with Bayesian updating. Specifically, when the relationship between the inputs and the target cannot be inferred from the examples alone, LLMs rely on domain-specific knowledge as the prior. Conversely, as contradicting examples accumulate, LLM predictions shift progressively towards these examples, which act as the likelihood. Our work reveals how LLMs use domain-specific knowledge in numerical prediction and positions them as data-efficient predictors for data-scarce scientific domains.

cs.AI · cs.CL · cs.LG

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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.491ACL Anthology · ACL 2026

GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models

Ziyang Wang, Jiangfeng Xiao Shenzhen University, Chuan Xiao Osaka University, Ruoxiang LI Shenzhen University, Rui Mao Shenzhen Institute of Computing Sciences and Shenzhen University, Jianbin Qin Shenzhen University

原文摘要

Large language models (LLMs) are expensive to serve because dense FFN blocks, multi-head attention, and KV caches dominate memory, making structured pruning a natural way to reduce serving costs under tight parameter and memory budgets. We present GRASPrune, a global budgeted structured pruning framework applied post-hoc to a pretrained model that jointly prunes FFN channels and attention KV head groups under a single global parameter budget. GRASPrune attaches lightweight learnable gates to prunable units and optimizes only these gates on a small unlabeled language-modeling calibration set, keeping all backbone weights frozen while enforcing the target sparsity at every step. A final budget-preserving scaling calibration reweights the surviving channels and heads to correct scale shifts introduced by pruning. On LLaMA-2-7B, GRASPrune removes 50% of parameters and achieves 12.18 perplexity on WikiText-2 while maintaining competitive average zero-shot accuracy on five downstream benchmarks, using a short calibration run of four epochs on 512 unlabeled sequences on a single NVIDIA A100 80GB GPU, all without any full-model fine-tuning.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.618ACL Anthology · ACL 2026

Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement

Qimin Zhong Microsoft Research Asia, Hao Liao Shenzhen University, Haiming Qin, Mingyang Zhou Shenzhen University, Rui Mao Shenzhen Institute of Computing Sciences and Shenzhen University, Wei Chen, Naipeng Chao

原文摘要

Whether Large Language Models (LLMs) develop coherent internal world models remains a core debate. While conventional Next-Token Prediction (NTP) focuses on one-step-ahead supervision, Multi-Token Prediction (MTP) has shown promise in learning more structured representations. In this work, we provide a theoretical perspective analyzing the gradient inductive bias of MTP, supported by empirical evidence, showing that MTP promotes the convergence toward internal belief states by inducing representational contractivity via gradient coupling. However, we reveal that standard MTP often suffers from structural hallucinations, where discrete token supervision encourages illegal shortcuts in latent space that violate environmental constraints. To address this, we propose a novel method Latent Semantic Enhancement MTP (LSE-MTP), which anchors predictions to ground-truth hidden state trajectories. Experiments on synthetic graphs and real-world Manhattan Taxi Ride show that LSE-MTP effectively bridges the gap between discrete tokens and continuous state representations, enhancing representation alignment, reducing structural hallucinations, and improving robustness to perturbations.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.692ACL Anthology · ACL 2026

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers

Linrui Ma, Chun Hei Lo Huawei Technologies Ltd., Xinyu Wang McGill University, McGill University, Peng Lu, Xihao Yuan Huawei Technologies Ltd., Hanting Chen Peking University, Kai Han Independent Researcher, Xinghao Chen Huawei Noah's Ark Lab 等 15 位作者

原文摘要

The quadratic computational cost of traditional attention mechanisms poses a major bottleneck to the scalability and practical deployment of large language models (LLMs), particularly in long-context scenarios. To improve efficiency, existing approaches often enforce rigid structural constraints such as local attention windows. However, these strategies typically lead to substantial performance degradation on tasks requiring precise long-range recall. In this work, we propose MATCH, a scalable and efficient framework that augments sparsified attention mechanisms with dynamically integrated in-context information through an efficient retrieval system. Empirical results show that MATCH significantly improves the performance of sparse-attention models on both synthetic and real-world natural-language tasks. These findings highlight the versatility of MATCH as a general approach for enhancing in-context retrieval capabilities while maintaining the efficiency benefits of sparse attention architectures.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.97ACL Anthology · ACL 2026

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

Xi Xiao University of Alabama at Birmingham, Chenrui Ma, Yunbei Zhang Tulane University, Chen Liu Yale University, Zhuxuanzi Wang Cornell University, Yanshu Li, Lin Zhao Northeastern University, Guosheng Hu University of Bristol 等 10 位作者

原文摘要

Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: \textit{semantic drift}, arising from treating all update directions with equal importance, and \textit{structural incoherence}, due to adapting layers independently, resulting in uncoordinated and suboptimal updates. To address these issues, we propose \textbf{StructLoRA}, a framework that tackles both limitations through a principled dual-component design: (1) an Information Bottleneck-guided filter that prunes task-irrelevant directions to mitigate semantic drift, and (2) a lightweight, training-only graph-based coordinator that enforces inter-layer consistency to resolve structural incoherence. Extensive experiments across large language models, vision language models, and vision models (including LLaMA, LLaVA, and ViT) demonstrate that \textbf{StructLoRA} consistently establishes a new state of the art, outperforming not only vanilla LoRA but also advanced dynamic rank allocation and sparsity-based methods. Notably, the gains are particularly pronounced in challenging low-rank and low-data regimes. Crucially, since the proposed modules operate only during training, \textbf{StructLoRA} improves performance with \textbf{zero additional inference cost}, shifting the focus of PEFT from mere parameter compression to a more holistic optimization of information quality and structural integrity.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表