Search Results for author: Jintao Tang

Found 6 papers, 3 papers with code

Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines

1 code implementation4 Mar 2024 Kehan Long, Shasha Li, Pancheng Wang, Chenlong Bao, Jintao Tang, Ting Wang

To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR.

Citation Recommendation Recommendation Systems

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

no code implementations11 Jul 2023 Chunxi Guo, Zhiliang Tian, Jintao Tang, Shasha Li, Zhihua Wen, Kaixuan Wang, Ting Wang

Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL.

Retrieval Text-To-SQL

Address Matching Based On Hierarchical Information

no code implementations10 May 2023 Chengxian Zhang, Jintao Tang, Ting Wang, Shasha Li

There is evidence that address matching plays a crucial role in many areas such as express delivery, online shopping and so on.

Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval

no code implementations26 Apr 2023 Chunxi Guo, Zhiliang Tian, Jintao Tang, Pancheng Wang, Zhihua Wen, Kang Yang, Ting Wang

Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database.

Informativeness Retrieval +2

Multi-Document Scientific Summarization from a Knowledge Graph-Centric View

1 code implementation COLING 2022 Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers.

Descriptive Knowledge Graphs

Cannot find the paper you are looking for? You can Submit a new open access paper.