Abstract Biomedical Entity Linking (BM-EL) task, which aims to match biomedical mentions
in articles to entities in a certain knowledge base (eg, the Unified Medical Language …

Advancements in LLMs have recently unveiled challenges tied to computational efficiency
and continual scalability due to their requirements of huge parameters, making the …

Injecting external knowledge can improve the performance of pre-trained language models
(PLMs) on various downstream NLP tasks. However, massive retraining is required to …

In recent years, large language models (LLMs), such as GPTs, have attained great impact
worldwide. However, how to adapt these LLMs to better suit the vertical domain-specific …

This study focuses on the evaluation of the Open Question Answering (Open-QA) task,
which can directly estimate the factuality of large language models (LLMs). Current …

Recent advancements in Natural Language Processing (NLP) have witnessed the
groundbreaking impact of pretrained models, yielding impressive outcomes across various …

Knowledge Graphs (KGs) often have two characteristics: heterogeneous graph structure and
text-rich entity/relation information. Text-based KG embeddings can represent entities by …

Abstract Commonsense Question Answering (CQA) aims to answer questions that require
human commonsense. Closed-book CQA, as one of the subtasks, requires the model to …

Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace
after the success of pre-training in the natural language domain. Despite the promising …

In recent years, the task of Relation Classification (RC) in scientific domains has received
widespread attention. During the fine-tuning phase of Pre-trained Language Models (PLMs) …