Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …

Large Language Models (LLMs) recently demonstrated extraordinary capability in various
natural language processing (NLP) tasks including language translation, text generation …

Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is
pivotal for advancing Large Language Models (LLMs). In this paper, we delve into the …

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether
LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains …

Within the evolving landscape of deep learning, the dilemma of data quantity and quality has
been a long-standing problem. The recent advent of Large Language Models (LLMs) offers …

Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …

Preference optimization, particularly through Reinforcement Learning from Human
Feedback (RLHF), has achieved significant success in aligning Large Language Models …

Large Language Models (LLMs) have democratized synthetic data generation, which in turn
has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a …

Data annotation and synthesis generally refers to the labeling or generating of raw data with
relevant information, which could be used for improving the efficacy of machine learning …

Large Language models (LLMs) are rapidly being adopted for tasks such as text
summarization, in a wide range of industries. This has driven the need for scalable …