CoreLM: Coreference-aware Language Model Fine-Tuning (2111.02687v1)

Abstract: LLMs are the underpin of all modern NLP tasks. The introduction of the Transformers architecture has contributed significantly into making LLMing very effective across many NLP task, leading to significant advancements in the field. However, Transformers come with a big computational cost, which grows quadratically with respect to the input length. This presents a challenge as to understand long texts requires a lot of context. In this paper, we propose a Fine-Tuning framework, named CoreLM, that extends the architecture of current Pretrained LLMs so that they incorporate explicit entity information. By introducing entity representations, we make available information outside the contextual space of the model, which results in a better LLM for a fraction of the computational cost. We implement our approach using GPT2 and compare the fine-tuned model to the original. Our proposed model achieves a lower Perplexity in GUMBY and LAMBDADA datasets when compared to GPT2 and a fine-tuned version of GPT2 without any changes. We also compare the models' performance in terms of Accuracy in LAMBADA and Children's Book Test, with and without the use of model-created coreference annotations.