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Status: Unchecked ×Keyword: knowledge consolidation ×Clear all

1 claim from 1 paper

1. Computer Science › Topic Modeling

   Knowledge Rumination for Pre-trained Language Models

   Yao, Wang, Mao et al. · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 2023

   The paper proposes Knowledge Rumination, which prompts a pre-trained language model to recall its own latent knowledge instead of retrieving it from outside, and reports better results on commonsense reasoning and GLUE.

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   1. UncheckedThe authors observe that pre-trained language models may already hold rich knowledge in their parameters yet not fully use it on knowledge-intensive tasks.“However, despite the promising outcome, we empirically observe that PLMs may have already encoded rich knowledge in their pre-trained parameters but fail to fully utilize them when applying them to knowledge-intensive tasks.”