14 citations · 73 across the 14 of their papers we have counts for
14 papers
Improving Dense Passage Retrieval with Multiple Positive Passages
Shuai Chang
By leveraging a dual encoder architecture, Dense Passage Retrieval (DPR) has outperformed traditional sparse retrieval algorithms such as BM25 in terms of passage retrieval accurac…
What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects
Naihao Deng, Sheng Zhang, Henghui Zhu +7
Table modeling has progressed for decades. In this work, we revisit this trajectory and highlight emerging challenges in the LLM era, particularly the paradox of choice: the diffic…
PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries
Mingwen Dong, Nischal Ashok Kumar, Yiqun Hu +9
Previous text-to-SQL datasets and systems have primarily focused on user questions with clear intentions that can be answered. However, real user questions can often be ambiguous w…
You Only Read Once (YORO): Learning to Internalize Database Knowledge for Text-to-SQL
Hideo Kobayashi, Wuwei Lan, Peng Shi +5
While significant progress has been made on the text-to-SQL task, recent solutions repeatedly encode the same database schema for every question, resulting in unnecessary high infe…
RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation
Dongyu Ru, Lin Qiu, Xiangkun Hu +15
Despite Retrieval-Augmented Generation (RAG) showing promising capability in leveraging external knowledge, a comprehensive evaluation of RAG systems is still challenging due to th…
Selective Demonstrations for Cross-domain Text-to-SQL
Shuaichen Chang, Eric Fosler-Lussier
Large language models (LLMs) with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task, without the use of in-domain an…
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
Shuaichen Chang, Eric Fosler-Lussier
Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task. Previous research has prompted LLMs with various demonstratio…
Dr.Spider: A Diagnostic Evaluation Benchmark towards Text-to-SQL Robustness
Shuaichen Chang, Jun Wang, Mingwen Dong +13
Neural text-to-SQL models have achieved remarkable performance in translating natural language questions into SQL queries. However, recent studies reveal that text-to-SQL models ar…
MapQA: A Dataset for Question Answering on Choropleth Maps
Shuaichen Chang, David Palzer, Jialin Li +2
Choropleth maps are a common visual representation for region-specific tabular data and are used in a number of different venues (newspapers, articles, etc). These maps are human-r…
Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions
Naihao Deng, Shuaichen Chang, Peng Shi +2
Existing text-to-SQL research only considers complete questions as the input, but lay-users might strive to formulate a complete question. To build a smarter natural language inter…
ExplainaBoard: An Explainable Leaderboard for NLP
Pengfei Liu, Jinlan Fu, Yang Xiao +7
With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal…
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Yusen Zhang, Xiangyu Dong, Shuaichen Chang +3
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any inpu…
Zero-shot Text-to-SQL Learning with Auxiliary Task
Shuaichen Chang, Pengfei Liu, Yun Tang +3
Recent years have seen great success in the use of neural seq2seq models on the text-to-SQL task. However, little work has paid attention to how these models generalize to realisti…
Contextualized Non-local Neural Networks for Sequence Learning
Pengfei Liu, Shuaichen Chang, Xuanjing Huang +2
Recently, a large number of neural mechanisms and models have been proposed for sequence learning, of which self-attention, as exemplified by the Transformer model, and graph neura…