TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation

no code implementations • 15 Feb 2024 • Yaoxiang Wang, Zhiyong Wu, Junfeng Yao, Jinsong Su

The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks.

IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models

no code implementations • 1 Nov 2023 • Xiaoyue Wang, Xin Liu, Lijie Wang, Yaoxiang Wang, Jinsong Su, Hua Wu

Then, we pair each sample with a bias indicator representing its bias degree, and use these extended samples to train a sample generator.

Natural Language Understanding

OpenICL: An Open-Source Framework for In-context Learning

3 code implementations • 6 Mar 2023 • Zhenyu Wu, Yaoxiang Wang, Jiacheng Ye, Jiangtao Feng, Jingjing Xu, Yu Qiao, Zhiyong Wu

However, the implementation of ICL is sophisticated due to the diverse retrieval and inference methods involved, as well as the varying pre-processing requirements for different models, datasets, and tasks.

In-Context Learning Language Modelling +4

Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering

1 code implementation • 20 Dec 2022 • Zhiyong Wu, Yaoxiang Wang, Jiacheng Ye, Lingpeng Kong

Despite the surprising few-shot performance of in-context learning (ICL), it is still a common practice to randomly sample examples to serve as context.

In-Context Learning