Search Results for author: Shuofei Qiao

Found 10 papers, 10 papers with code

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents

1 code implementation5 Mar 2024 Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Ningyu Zhang, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges, especially when interacting with environments through generating executable actions.

Hallucination Self-Learning

EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models

3 code implementations5 Feb 2024 Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Yida Xue, Runnan Fang, Kangwei Liu, Lei LI, Zhen Bi, Guozhou Zheng, Huajun Chen

In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs).

AUTOACT: Automatic Agent Learning from Scratch via Self-Planning

1 code implementation10 Jan 2024 Shuofei Qiao, Ningyu Zhang, Runnan Fang, Yujie Luo, Wangchunshu Zhou, Yuchen Eleanor Jiang, Chengfei Lv, Huajun Chen

Further analysis demonstrates the effectiveness of the division-of-labor strategy, with the trajectory quality generated by AutoAct significantly outperforming that of others.

Question Answering

LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities

1 code implementation22 May 2023 Yuqi Zhu, Xiaohan Wang, Jing Chen, Shuofei Qiao, Yixin Ou, Yunzhi Yao, Shumin Deng, Huajun Chen, Ningyu Zhang

We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link prediction, and question-answering, thereby thoroughly exploring LLMs' performance in the domain of construction and inference.

Event Extraction graph construction +4

Making Language Models Better Tool Learners with Execution Feedback

1 code implementation22 May 2023 Shuofei Qiao, Honghao Gui, Chengfei Lv, Qianghuai Jia, Huajun Chen, Ningyu Zhang

To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively.

Language Modelling Large Language Model +1

InstructIE: A Bilingual Instruction-based Information Extraction Dataset

3 code implementations19 May 2023 Honghao Gui, Shuofei Qiao, Jintian Zhang, Hongbin Ye, Mengshu Sun, Lei Liang, Jeff Z. Pan, Huajun Chen, Ningyu Zhang

Large language models can perform well on general natural language tasks, but their effectiveness is still not optimal for information extraction.

One Model for All Domains: Collaborative Domain-Prefix Tuning for Cross-Domain NER

2 code implementations25 Jan 2023 Xiang Chen, Lei LI, Shuofei Qiao, Ningyu Zhang, Chuanqi Tan, Yong Jiang, Fei Huang, Huajun Chen

Previous typical solutions mainly obtain a NER model by pre-trained language models (PLMs) with data from a rich-resource domain and adapt it to the target domain.

NER Text Generation

Reasoning with Language Model Prompting: A Survey

2 code implementations19 Dec 2022 Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen

Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.

Arithmetic Reasoning Common Sense Reasoning +4

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