As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

Augmenting Large Language Models (LLMs) for Question Answering (QA) with domain
specific data has attracted wide attention. However, domain data often exists in a hybrid …

Retrieval-augmented generation enhances large language models (LLMs) by incorporating
relevant information from external knowledge sources. This enables LLMs to adapt to …

Event temporal relation (TempRel) is a primary subject of the event relation extraction task.
However, the inherent ambiguity of TempRel increases the difficulty of the task. With the rise …

This work presents a dynamic vocabulary adaptation strategy, MEDVOC, for fine-tuning pre-
trained language models (PLMs) like BertSumAbs, BART, and PEGASUS for improved …

Retrieval augmented generation (RAG) for technical documents creates challenges as
embeddings do not often capture domain information. We review prior art for important …

Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between
domains by transferring knowledge from labeled source graphs to given unlabeled target …

Cross-domain constituency parsing remains a challenging task due to the lack of high-
quality out-of-domain data. In this paper, we propose a data augmentation method via …

The emergence of ChatGPT in late 2022 has rendered generative discourse models
essential to daily living. The increasing user expectations have elevated the necessity to …

Questions are critical for information-seeking and learning. Automatic Question Generation
(AQG) involves the subjects of Information Retrieval (IR) and Natural Language Processing …