Recently, ChatGPT, along with DALL-E-2 and Codex, has been gaining significant attention
from society. As a result, many individuals have become interested in related resources and …

Pre-trained language models have achieved striking success in natural language
processing (NLP), leading to a paradigm shift from supervised learning to pre-training …

Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper,
we investigate two recently proposed pretrained language models (PLMs) and analyze the …

Existing pre-trained models for knowledge-graph-to-text (KG-to-text) generation simply fine-
tune text-to-text pre-trained models such as BART or T5 on KG-to-text datasets, which …

Despite recent improvements in abstractive summarization, most current approaches
generate summaries that are not factually consistent with the source document, severely …

Relation prediction on knowledge graphs (KGs) aims to infer missing valid triples from
observed ones. Although this task has been deeply studied, most previous studies are …

Pretrained language models (PLM) have recently advanced graph-to-text generation, where
the input graph is linearized into a sequence and fed into the PLM to obtain its …

The linear sequence of amino acids determines protein structure and function. Protein
design, known as the inverse of protein structure prediction, aims to obtain a novel protein …

This paper studies how to automatically generate a natural language text that describes the
facts in knowledge graph (KG). Considering the few-shot setting, we leverage the excellent …

Large language models (LLMs) have been widely employed for graph-to-text generation
tasks. However, the process of finetuning LLMs requires significant training resources and …