Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On
  Wikidata using LM probing
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%.
66cf0cf9679d36a5a9eed1d6
Hanna Abi Akl
claimed_verified
2024-08-28T13:47:04.297Z
60e9d2e4e844d12533b9fd22
https://cdn-avatars.huggingface.co/v1/production/uploads/60e9d2e4e844d12533b9fd22/qs9kjTEC-0QbEAOPShygi.jpeg
Hanna Abi Akl
user
HannaAbiAkl
Project SHADOW: Symbolic Higher-order Associative Deductive reasoning On
  Wikidata using LM probing
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%.
SHADOW, a fine-tuned language model for associative deductive reasoning, outperforms the baseline in the LM-KBC 2024 challenge with a 20% improvement in F1 score.
fine-tuned language model associative deductive reasoning knowledge base construction Wikidata triple completion LM-KBC 2024 F1 score