Large knowledge model: Perspectives and challenges

H Chen - arXiv preprint arXiv:2312.02706, 2023 - arxiv.org
Humankind's understanding of the world is fundamentally linked to our perception and
cognition, with\emph {human languages} serving as one of the major carriers of\emph {world …

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction

Z Li, Y Zeng, Y Zuo, W Ren, W Liu, M Su, Y Guo… - arXiv preprint arXiv …, 2024 - arxiv.org
In this paper, we propose KnowCoder, a Large Language Model (LLM) to conduct Universal
Information Extraction (UIE) via code generation. KnowCoder aims to develop a kind of …

EVIT: Event-Oriented Instruction Tuning for Event Reasoning

Z Tao, X Chen, Z Jin, X Bai, H Zhao, Y Lou - arXiv preprint arXiv …, 2024 - arxiv.org
Events refer to specific occurrences, incidents, or happenings that take place under a
particular background. Event reasoning aims to infer events according to certain relations …

Knowledge Editing for Large Language Models

N Zhang, Y Yao, S Deng - Proceedings of the 2024 Joint …, 2024 - aclanthology.org
Even with their impressive abilities, Large Language Models (LLMs) such as ChatGPT are
not immune to issues of factual or logically consistent. Concretely, the key concern is how to …

BianCang: A Traditional Chinese Medicine Large Language Model

S Wei, X Peng, Y Wang, J Si, W Zhang, W Lu… - arXiv preprint arXiv …, 2024 - arxiv.org
The rise of large language models (LLMs) has driven significant progress in medical
applications, including traditional Chinese medicine (TCM). However, current medical LLMs …

Qwen-IG: A Qwen-based Instruction Generation Model for LLM Fine-tuning

L Zhang, Y Liu, Y Luo, F Gao, J Gu - Proceedings of the 2024 13th …, 2024 - dl.acm.org
The quality of instructions is crucial for LLM (large language model) fine-tuning. The most
compelling data for instruction tuning exhibit not only high complexity, low perplexity, high …

Overcoming Catastrophic Forgetting: A Novel Fine-Tuning Method

F Ding - openreview.net
Despite remarkable advances in Large Language Models (LLMs), a persistent challenge
remains: the potential for these models to acquire erroneous or outdated information from …