← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1355 · 记录 #1
Mitigating Structural Knowledge Collapse in Domain-Specific LLMs via Morpheme-Aware KV-Aggregation
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:54
摘要
Standard tokenizers over-fragment domain terms, disrupting morpheme semantics. We characterize this representational misalignment as Structural Knowledge Collapse (SKC), where attention mechanisms fail to reconstruct coherent concepts from fragmented inputs. While existing input-centric solutions like vocabulary expansion address this, they necessitate expensive embedding retraining and neglect internal attention compositionality. To this end, we introduce Morpheme-aware KV-aggregation Attention (MorphKA), a lightweight adapter that dynamically consolidates fragments without tokenizer changes. Bypassing tokenizer retraining, MorphKA employs a dual-phase strategy, Input-Level Morpheme Aggregation (IMA) and Context-Aware KV-Aggregation (AMRF), to stabilize morpheme spans and synthesize higher-order concepts. Experiments on medical and legal benchmarks show MorphKA outperforms vocabulary adaptation baselines by 3.2–4.6%, reaching 7.9% on high-fragmentation terms. Moreover, MorphKA reduces catastrophic interference on general capabilities by 18–22% with ~80% fewer parameters than embedding retraining approaches.
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记录 #1 · 2026-07 · 最新观测版本
Mitigating Structural Knowledge Collapse in Domain-Specific LLMs via Morpheme-Aware KV-Aggregation
Standard tokenizers over-fragment domain terms, disrupting morpheme semantics. We characterize this representational misalignment as Structural Knowledge Collapse (SKC), where attention mechanisms fail to reconstruct coherent concepts from fragmented inputs. While existing input-centric solutions like vocabulary expansion address this, they necessitate expensive embedding retraining and neglect internal attention compositionality. To this end, we introduce Morpheme-aware KV-aggregation Attention (MorphKA), a lightweight adapter that dynamically consolidates fragments without tokenizer changes. Bypassing tokenizer retraining, MorphKA employs a dual-phase strategy, Input-Level Morpheme Aggregation (IMA) and Context-Aware KV-Aggregation (AMRF), to stabilize morpheme spans and synthesize higher-order concepts. Experiments on medical and legal benchmarks show MorphKA outperforms vocabulary adaptation baselines by 3.2–4.6%, reaching 7.9% on high-fragmentation terms. Moreover, MorphKA reduces catastrophic interference on general capabilities by 18–22% with ~80% fewer parameters than embedding retraining approaches.
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