Relation extraction (RE) involves identifying the relations between entities from underlying
content. RE serves as the foundation for many natural language processing (NLP) and …

Abstract Large Language Models (LLMs) have demonstrated impressive performance in
various NLP tasks, but they still suffer from challenges such as hallucination and weak …

A wide variety of NLP applications, such as machine translation, summarization, and dialog,
involve text generation. One major challenge for these applications is how to evaluate …

The task of relation extraction is about identifying entities and relations among them in free
text for the enrichment of structured knowledge bases (KBs). In this paper, we present a …

In this paper, we present a conceptually simple while empirically powerful framework for
abstractive summarization, SimCLS, which can bridge the gap between the learning …

To assess the effectiveness of any medical intervention, researchers must conduct a time-
intensive and highly manual literature review. NLP systems can help to automate or assist in …

The rapid development of science and technology has been accompanied by an
exponential growth in peer-reviewed scientific publications. At the same time, the review of …

The recent advances in large language models (LLM) and foundation models with emergent
capabilities have been shown to improve the performance of many NLP tasks. LLMs and …

Motivated by the fact that many relations cross the sentence boundary, there has been
increasing interest in document-level relation extraction (DocRE). DocRE requires …

We present a simulation of various active learning strategies for the discovery of polymer
solar cell donor/acceptor pairs using data extracted from the literature spanning∼ 20 years …