Building Language Models for Text with Named Entities

Abstract: Text in many domains involves a significant amount of named entities. Predict- ing the entity names is often challenging for a LLM as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a discriminative LLM which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evalu- ate the proposed model. Experimental re- sults show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than the state-of-the-art LLMs.