23 citations · 129 across the 19 of their papers we have counts for
9 papers · 1 filter
Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation
Yuanyuan Liang, Jianing Wang, Hanlun Zhu +3
The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. For the sake of expensive cost of large-scale question…
LLM4Vis: Explainable Visualization Recommendation using ChatGPT
Lei Wang, Songheng Zhang, Yun Wang +2
Data visualization is a powerful tool for exploring and communicating insights in various domains. To automate visualization choice for datasets, a task known as visualization reco…
Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
Lei Wang, Wanyu Xu, Yihuai Lan +4
Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT)…
Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media
Wei Qin, Zetong Chen, Lei Wang +3
This paper proposes a new depression detection system based on LLMs that is both interpretable and interactive. It not only provides a diagnosis, but also diagnostic evidence and p…
Non-Autoregressive Math Word Problem Solver with Unified Tree Structure
Yi Bin, Mengqun Han, Wenhao Shi +4
Existing MWP solvers employ sequence or binary tree to present the solution expression and decode it from given problem description. However, such structures fail to handle the var…
T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Mixed Large Language Model Signals for Science Question Answering
Lei Wang, Yi Hu, Jiabang He +4
Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chai…
Zero-Shot Next-Item Recommendation using Large Pretrained Language Models
Lei Wang, Ee-Peng Lim
Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference witho…
LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models
Zhiqiang Hu, Lei Wang, Yihuai Lan +6
The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetunin…
ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction
Jiabang He, Lei Wang, Yi Hu +4
Large language models (LLMs), such as GPT-3 and ChatGPT, have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning, which inv…