arXiv cs.AI· Rosario Uceda-Sosa, Maria Chang, Karthikeyan Natesan Ramamurthy, Moninder Singh·· 2 小时前AI 评分41
范围化语义不变性：提升 LLM 可靠性的基础研究（EMNLP 2026 论文）
Scoped Semantic Invariance as a Foundation for LLM Reliability
AI 导读
一项 EMNLP 2026 论文将 LLM 对语义等价查询回答不一致的问题形式化为语义不变性，并证明任意保义变换下的不变性不可判定，由此提出以知识图谱为控制结构的范围化语义不变性框架。
正文 · 原文
Abstract:LLMs often answer semantically equivalent queries inconsistently, even when the underlying facts are known. We formalize consistency as semantic invariance over equivalence classes of queries and show that invariance under arbitrary meaning-preserving transformations is undecidable in general. This motivates scoped semantic invariance, where model behavior is evaluated relative to bounded semantic domains.
We operationalize this framework using knowledge graphs (KGs), which act as explicit semantic control structures defining local semantic constraints and grounding contexts. Across the MITRE D3FEND ontology and eight Wikidata- derived domains, we show that inconsistencies are structured rather than random, concentrating in localized hotspot subgraphs organized around recurring TREE, STAR, and CYCLE patterns that remain stable across models and domains.
We further show that selective graph grounding improves correctness and cross-model consistency, while indiscriminate grounding introduces regressions. A layered grounding strategy achieves the strongest monotonicity profile, preserving correct knowledge while reducing inconsistency. Finally, we show that hotspot structures discovered from simple consistency probes transfer to more realistic compositional reasoning tasks.
| Comments: | Accepted at EMNLP 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33267 [cs.AI] |
| (or arXiv:2610.33267v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33267 arXiv-issued DOI via DataCite |
Submission history
From: Rosario Uceda-Sosa PhD [view email]
[v1]
Fri, 23 Oct 2026 20:26:21 UTC (4,590 KB)
来源：arXiv cs.AI · arxiv.org