Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the
hallucination of large language models (LLMs). However, existing research lacks rigorous …

With direct access to human-written reference as memory, retrieval-augmented generation
has achieved much progress in a wide range of text generation tasks. Since better memory …

Recently, retrieval-augmented text generation attracted increasing attention of the
computational linguistics community. Compared with conventional generation models …

Recent advances in Named Entity Recognition (NER) show that document-level contexts
can significantly improve model performance. In many application scenarios, however, such …

As Large Language Models (LLMs) become popular, there emerged an important trend of
using multimodality to augment the LLMs' generation ability, which enables LLMs to better …

Recent studies have shown the effectiveness of retrieval augmentation in many generative
NLP tasks. These retrieval-augmented methods allow models to explicitly acquire prior …

Prior work has proved that Translation memory (TM) can boost the performance of Neural
Machine Translation (NMT). In contrast to existing work that uses bilingual corpus as TM and …

Simulation plays a crucial role in the development of autonomous vehicles (AVs) due to the
potential risks associated with real-world testing. Although significant progress has been …

Personalized dialogue systems explore the problem of generating responses that are
consistent with the user's personality, which has raised much attention in recent years …

Event argument extraction has long been studied as a sequential prediction problem with
extractive-based methods, tackling each argument in isolation. Although recent work …