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Research map / Large language models / Retrieval and knowledge

Faithful Embeddings for Knowledge Base Queries

NeurIPS 2020 · Large language models › Retrieval and knowledge · approach: Knowledge graph reasoning with LLMs

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use EmQL instead of Query2BoxKB query embedding · FB15k entailmentHits@3 (Avg): 43.7 -> 91.4Helped
Use EmQL instead of Query2BoxKB query embedding · FB15k-237 entailmentHits@3 (Avg): 36.2 -> 94.2Helped
Use EmQL instead of Query2BoxKB query embedding · NELL995 entailmentHits@3 (Avg): 51.1 -> 98.8Helped
Use EmQL instead of Query2BoxKB query embedding · FB15k generalizationHits@3 (Avg): 48.4 -> 49.5Mixed
Use EmQL instead of Query2BoxKB query embedding · FB15k-237 generalizationHits@3 (Avg): 26.8 -> 35.8Helped
Use EmQL instead of Query2BoxKB query embedding · NELL995 generalizationHits@3 (Avg): 30.6 -> 46.8Helped
Add count-min sketches to the set representation (ablation)EmQL · FB15k entailmentHits@3 (Avg): 55.5 -> 91.4Helped
Add count-min sketches to the set representation (ablation)EmQL · FB15k-237 entailmentHits@3 (Avg): 57.4 -> 94.2Helped
Add count-min sketches to the set representation (ablation)EmQL · NELL995 entailmentHits@3 (Avg): 82.5 -> 98.8Helped
Add count-min sketches throughout the QA model (ablation)EmQL on WebQuestionsSP · WebQuestionsSPHits@1: 53.2 -> 75.5Helped
Add relational filtering (ablation)EmQL on WebQuestionsSP · WebQuestionsSPHits@1: 65.2 -> 75.5Helped
Use exact rather than approximate MIPS (ablation)EmQL on WebQuestionsSP · WebQuestionsSPHits@1: 73.4 -> 75.5Helped

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