title: Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On
    Wikidata using LM probing
summary: We introduce SHADOW, a fine-tuned language model trained on an intermediate
  task using associative deductive reasoning, and measure its performance on a
  knowledge base construction task using Wikidata triple completion. We evaluate
  SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline
  solution by 20% with a F1 score of 68.72%.
paper_authors._id: 66cf0cf9679d36a5a9eed1d6
paper_authors.name: Hanna Abi Akl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-28T13:47:04.297Z
paper_authors._id: 60e9d2e4e844d12533b9fd22
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/60e9d2e4e844d12533b9fd22/qs9kjTEC-0QbEAOPShygi.jpeg
paper_authors.fullname: Hanna Abi Akl
paper_authors.type: user
paper_authors.user: HannaAbiAkl
paper_title: Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On
    Wikidata using LM probing
paper_summary: We introduce SHADOW, a fine-tuned language model trained on an intermediate
  task using associative deductive reasoning, and measure its performance on a
  knowledge base construction task using Wikidata triple completion. We evaluate
  SHADOW on the LM-KBC 2024 challenge and show that it outperforms the baseline
  solution by 20% with a F1 score of 68.72%.
paper_ai_keywords: SHADOW fine-tuned language model associative deductive reasoning knowledge base construction Wikidata triple completion LM-KBC 2024 challenge F1 score