KARMA：基于知识图谱的自动推理实例化与对齐
KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignment
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中文总结 AI 辅助
研究针对基于模板对比合成的问题，提出KARMA方法，通过枚举知识图谱路径生成对比候选，用解耦槽级目标进行偏好监督，在多领域基准测试中表现优于基线。
中文摘要 AI 辅助
基于模板的对比合成具有可扩展性，但其候选通常仅在少数实体槽上不同，而序列级优化将监督分散在大多共享模板上。我们将此形式化为分辨率不匹配问题并提出KARMA，它在领域知识图谱上枚举模式约束路径并将其转化为槽对齐的对比候选。槽并行对齐（SPA）然后应用解耦的槽级目标将偏好监督路由到有区分性的实体槽，槽感知掩码注意力作为可选的打包评估实现。在生物医学、计算机科学和化学基准测试中，KARMA优于基础语言模型和相同数据的监督微调基线，并与序列和令牌级偏好方法相比具有优势。
英文摘要
Template-based contrastive synthesis is scalable, but its candidates often differ only in a few entity-slots while sequence-level optimization spreads supervision over mostly shared templates. We formalize this as the Resolution Mismatch Problem and propose KARMA, which enumerates schema-constrained paths over domain knowledge graphs and verbalizes them into slot-aligned contrastive candidates. Slot-Parallel Alignment (SPA) then applies a decoupled slot-level objective to route preference supervision to discriminative entity-slots, with slot-aware masked attention serving as an optional packed-evaluation implementation. Across biomedical, computer-science, and chemistry benchmarks, KARMA outperforms base LLM and same-data SFT baselines, and compares favorably with sequence- and token-level preference methods.
发表机构
- Sejong University（世宗大学）
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