Research Papers 论文研究 5h ago Updated 1h ago 更新于 1小时前 44

Patterns of Priming in Production: Lexical, Semantic and Structural Alignment in Language Model Generation 生成中的启动模式:语言模型生成中的词汇、语义和结构对齐

Language models exhibit structural priming in production, where preceding structural context influences sentence completion choices Priming effects are strongest in semantically coherent sentences, with lexico-semantic coherence boosting structural alignment Inverse frequency effects observed: greater relative increase in less frequent double-object datives, but larger absolute increase in more frequent prepositional-object constructions Structurally primed completions show elevated lexico-seman 语言模型在句子生成过程中确实存在结构启动效应,先前结构上下文会显著影响后续句子补全 结构启动在语义连贯的句子中更为显著,且启动后的补全会呈现更高水平的词汇-语义重复 双及物结构相对增幅更大(符合逆频率效应),但介词宾语结构作为更频繁产生的形式,其绝对增幅更高 结构启动在多个语言表征层面协同运作,由句法、词汇和语义对齐共同促进

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Impact 影响力

Analysis 深度分析

TL;DR

  • Language models exhibit structural priming in production, where preceding structural context influences sentence completion choices
  • Priming effects are strongest in semantically coherent sentences, with lexico-semantic coherence boosting structural alignment
  • Inverse frequency effects observed: greater relative increase in less frequent double-object datives, but larger absolute increase in more frequent prepositional-object constructions
  • Structurally primed completions show elevated lexico-semantic repetition, indicating cross-level alignment
  • Evidence supports that structural priming in LMs operates across syntactic, lexical, and semantic representation levels simultaneously

Why It Matters

This research bridges a critical gap between psycholinguistic findings on structural priming in human comprehension and their manifestation in AI language model production. For AI practitioners, understanding these alignment patterns is essential for controlling generation behavior, improving prompt design, and building more predictable and interpretable language systems. The findings also inform debates about whether LMs genuinely acquire linguistic structure or merely simulate it through statistical patterns.

Technical Details

  • Controlled sentence-completion experiments focused on dative construction alternations (double-object vs. prepositional-object) to test structural priming in LM production
  • Compared primed conditions against baseline conditions to measure relative and absolute priming magnitudes across construction types
  • Analyzed the interaction between structural priming and lexico-semantic coherence, finding that coherence amplifies priming effects
  • Examined repetition patterns in primed completions, demonstrating that structural alignment co-occurs with lexical and semantic alignment
  • Published on arXiv (2609.04484) in the Computation and Language (cs.CL) and Artificial Intelligence (cs.AI) categories

Industry Insight

  • Prompt engineering strategies should account for structural priming effects, as prior context can systematically bias generation toward specific syntactic patterns, affecting output consistency in production systems
  • The cross-level alignment phenomenon suggests that interventions targeting lexical or semantic consistency may indirectly influence syntactic choices, enabling more holistic control over generation behavior
  • As LMs are deployed in high-stakes language tasks, understanding priming mechanisms is critical for detecting and mitigating unwanted bias propagation through structural repetition in conversational and generative pipelines

TL;DR

  • 语言模型在句子生成过程中确实存在结构启动效应,先前结构上下文会显著影响后续句子补全
  • 结构启动在语义连贯的句子中更为显著,且启动后的补全会呈现更高水平的词汇-语义重复
  • 双及物结构相对增幅更大(符合逆频率效应),但介词宾语结构作为更频繁产生的形式,其绝对增幅更高
  • 结构启动在多个语言表征层面协同运作,由句法、词汇和语义对齐共同促进

为什么值得看

这篇研究揭示了语言模型在生成过程中如何受到先前结构的影响,为理解模型的语言处理能力提供了重要实证依据。对于AI从业者而言,这有助于优化模型的结构对齐能力,提升生成文本的自然度和一致性。

技术解析

研究通过控制性句子完成实验,聚焦于双及物结构(dative constructions)来检验结构启动效应,验证了LM在生成阶段(而非仅理解阶段)存在启动效应。

实验发现,虽然双及物结构相对增幅更大(符合逆频率效应),但介词宾语结构作为更频繁产生的形式,其绝对增幅更高,揭示了频率效应的双重表现。

结构启动不仅受到词汇-语义连贯性的增强,而且启动后的补全内容也显示出更高水平的词汇-语义重复,表明多层面对齐的协同机制。

研究结论支持结构启动在多个语言表征层面运作,句法、词汇和语义对齐相互促进,形成正反馈循环。

行业启示

  • 模型生成质量可通过优化上下文结构一致性来提升,提示词工程中应重视结构对齐策略
  • 逆频率效应与绝对频率效应的并存提示:在模型调优时需同时考虑相对增益和绝对产出量
  • 多层面语言表征的协同机制为改进模型连贯性和自然度提供了新的优化方向

Disclaimer: The above content is generated by AI and is for reference only. 免责声明:以上内容由 AI 生成,仅供参考。

LLM 大模型 Research 科学研究 Alignment 对齐 Evaluation 评测