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arXiv 最近完成扫描:2026-11-15 02:35 · 全站最近入库:2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

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版本更新 · v2arXiv:2611.13597

Compute the Interactions, Not the Attributes: What a Fact Layer Must Supply in the Chinese Sexagenary System

Huixue Han, Jiaying Liu

原文摘要

Language-model systems in rule-governed domains often pair the model with a deterministic fact layer, but what that layer should compute is rarely measured. We measure it in the Chinese sexagenary system underlying BaZi chart analysis, where every fact has one table-checkable value: 682 items over twelve fact categories and 160 transit--natal collision items, each posed on a ladder of rungs that differ only in how much of the computation is done for the model. Answering directly, none of eleven open-weight models gets more than 4\% of sexagenary days or 1\% of collisions right, and supplying the attributes of every pillar does not change the collisions. A complete list of computed relations is not enough either: it leaves Qwen3-32B at 1\%, with relations listed for other pairs misattributed to the queried pair, whereas a query-filtered layer from follow-up runs, stating only the queried relations pillar by pillar, gives 92\%. On chess positions, two predictions locked in advance hold: attributes do not help, and unqueried relations are again misattributed. Three proprietary reasoning models compute 68--95\% of collisions unaided, while reasoning in smaller open models overrides supplied facts. A fact layer should compute the interactions and return the ones the question asks about.

cs.CL

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.842ACL Anthology · ACL 2026

Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning

Yitong Wang China Mobile Jiutian Artificial Intelligence Technology (Beijing) Co., Ltd., Xue Han, WenChun Gao, Qian Hu China Mobile Research Institute, Jiahui Wang China Mobile Jiutian AI Technology (Beijing) Co., Ltd., Ziqing Wang, Lijun Mei China Mobile Communications Company Limited Research Institute, Junlan Feng

原文摘要

When large language models are used in real-world scenarios, continual learning (CL) becomes a non-trivial problem. In particular, continual learning with modern LLMs is challenged both by the substantial computational costs induced by their massive parameter scale, and by the limitations of current CL methods, which are mainly designed to mitigate catastrophic forgetting while neglecting knowledge sharing across tasks. We further observe that models with stronger performance exhibit stronger inter-task connections. In light of the above challenges and findings, we propose Attribution Scores-based Soft Orthogonality Low-Rank Adaptation (ASO-LoRA), an effective and efficient framework that simultaneously facilitates knowledge transfer while mitigating catastrophic forgetting. Specifically, ASO-LoRA initially assigns task-specific parameter subspaces for new tasks utilizing multi-LoRA modules, enabling for efficient training and inference without relying on task labels. Then, ASO-LoRA leverages attribution scores to evaluate task similarity and employs soft orthogonality between task-specific subspaces, guiding gradient updates in directions that promote parameter isolation, achieving a balance between knowledge transfer and preservation. Experiments are carried out on both the T5-large and the LLaMA2-7B, showing ASO-LoRA’s superior performance and suitability as a plug-in CL solution for general Transformer-based LLMs. Code is available at https://github.com/736619821/ASO-LORA.

cs.CL

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