Knowledge-Aware Language Model Pretraining
This work addresses the challenge of improving knowledge acquisition in language models for NLP applications, offering a simple, incremental enhancement to existing pretraining methods.
The paper tackled the problem of enhancing knowledge retention in pretrained language models by incorporating entity signals during pretraining, resulting in improved language modeling accuracy, factual correctness in LAMA tasks, and better performance in zero-shot question-answering without architectural changes.
How much knowledge do pretrained language models hold? Recent research observed that pretrained transformers are adept at modeling semantics but it is unclear to what degree they grasp human knowledge, or how to ensure they do so. In this paper we incorporate knowledge-awareness in language model pretraining without changing the transformer architecture, inserting explicit knowledge layers, or adding external storage of semantic information. Rather, we simply signal the existence of entities to the input of the transformer in pretraining, with an entity-extended tokenizer; and at the output, with an additional entity prediction task. Our experiments show that solely by adding these entity signals in pretraining, significantly more knowledge is packed into the transformer parameters: we observe improved language modeling accuracy, factual correctness in LAMA knowledge probing tasks, and semantics in the hidden representations through edge probing.We also show that our knowledge-aware language model (KALM) can serve as a drop-in replacement for GPT-2 models, significantly improving downstream tasks like zero-shot question-answering with no task-related training.