1 code implementation • 21 Feb 2024 • Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu
Large Language Models (LLMs) have revolutionized open-domain dialogue agents but encounter challenges in multi-character role-playing (MCRP) scenarios.
2 code implementations • 15 Nov 2023 • Yifan Wei, Xiaoyan Yu, Huanhuan Ma, Fangyu Lei, Yixuan Weng, Ran Song, Kang Liu
Knowledge Editing (KE) for modifying factual knowledge in Large Language Models (LLMs) has been receiving increasing attention.
no code implementations • 23 Oct 2023 • Fangyu Lei, Tongxu Luo, Pengqi Yang, Weihao Liu, Hanwen Liu, Jiahe Lei, Yiming Huang, Yifan Wei, Shizhu He, Jun Zhao, Kang Liu
Table-based question answering (TableQA) is an important task in natural language processing, which requires comprehending tables and employing various reasoning ways to answer the questions.
1 code implementation • 15 Oct 2023 • Huanhuan Ma, Weizhi Xu, Yifan Wei, Liuji Chen, Qiang Liu, Shu Wu, Liang Wang
Each instance is accompanied by a veracity label and an explanation that outlines the reasoning path supporting the veracity classification.
1 code implementation • 8 Oct 2023 • Yifan Wei, Yisong Su, Huanhuan Ma, Xiaoyan Yu, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu
As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning.
1 code implementation • 19 May 2023 • Fangyu Lei, Xiang Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, Kang Liu
In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner.
1 code implementation • 5 May 2023 • Yifan Wei, Fangyu Lei, Yuanzhe Zhang, Jun Zhao, Kang Liu
Hybrid question answering (HybridQA) over the financial report contains both textual and tabular data, and requires the model to select the appropriate evidence for the numerical reasoning task.