23 citations · 129 across the 19 of their papers we have counts for
19 papers
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…
Generalizing Math Word Problem Solvers via Solution Diversification
Zhenwen Liang, Jipeng Zhang, Lei Wang +3
Current math word problem (MWP) solvers are usually Seq2Seq models trained by the (one-problem; one-solution) pairs, each of which is made of a problem description and a solution s…
Alignment-Enriched Tuning for Patch-Level Pre-trained Document Image Models
Lei Wang, Jiabang He, Xing Xu +2
Alignment between image and text has shown promising improvements on patch-level pre-trained document image models. However, investigating more effective or finer-grained alignment…
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Weijieying Ren, Lei Wang, Kunpeng Liu +3
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-t…
Explanation Guided Contrastive Learning for Sequential Recommendation
Lei Wang, Ee-Peng Lim, Zhiwei Liu +1
Recently, contrastive learning has been applied to the sequential recommendation task to address data sparsity caused by users with few item interactions and items with few user ad…
MWPToolkit: An Open-Source Framework for Deep Learning-Based Math Word Problem Solvers
Yihuai Lan, Lei Wang, Qiyuan Zhang +5
Developing automatic Math Word Problem (MWP) solvers has been an interest of NLP researchers since the 1960s. Over the last few years, there are a growing number of datasets and de…
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving
Zhenwen Liang, Jipeng Zhang, Lei Wang +4
Math word problem (MWP) solving faces a dilemma in number representation learning. In order to avoid the number representation issue and reduce the search space of feasible solutio…
Investigating Math Word Problems using Pretrained Multilingual Language Models
Minghuan Tan, Lei Wang, Lingxiao Jiang +1
In this paper, we revisit math word problems~(MWPs) from the cross-lingual and multilingual perspective. We construct our MWP solvers over pretrained multilingual language models u…
DeepStyle: User Style Embedding for Authorship Attribution of Short Texts
Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang +2
Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have sho…
Translating a Math Word Problem to an Expression Tree
Lei Wang, Yan Wang, Deng Cai +2
Sequence-to-sequence (SEQ2SEQ) models have been successfully applied to automatic math word problem solving. Despite its simplicity, a drawback still remains: a math word problem c…
Beyond the limit of the least squares resolution and the lucky model
Gregorio Landi, Giovanni E. Landi
A very simple Gaussian model is used to illustrate a new fitting result: a linear growth of the resolution with the number N of detecting layers. This rule is well beyond the well-…