Papers by Hanchong Zhang

5 papers
CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions (2024.naacl-long)

Copied to clipboard

Challenge: Recent studies have demonstrated that Large Language Models (LLMs) have impressive capabilities in a variety of domains and tasks.
Approach: They propose a method which prompts LLMs to generate SQL queries based on the previously generated SQL query with an edition chain.
Outcome: The proposed method outperforms different in-context learning baselines and achieves state-of-the-art performance on two benchmarks SParC and CoSQL using LLMs.
Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)

Copied to clipboard

Challenge: Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated.
Approach: They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.
Outcome: The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering.
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset (2023.findings-acl)

Copied to clipboard

Challenge: a cross-domain text-to-SQL task aims to parse user questions into SQL on complete unseen databases . a single-domain task evaluates the performance on identical databases based on the same domain .
Approach: They propose a cross-domain text-to-SQL task that parses user questions into SQL on unseen databases.
Outcome: The proposed system can parse user questions into SQL on complete unseen databases.
ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought (2023.findings-emnlp)

Copied to clipboard

Challenge: Recent studies have focused on the development of semantic parsers within the framework of cross-domain analysis.
Approach: They propose a method to generate auto-CoT exemplars using ACT-SQL and extend it to multi-turn text-to-Sql tasks.
Outcome: The proposed method achieves SOTA performance on the Spider dev set among existing in-context learning approaches.
NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)

Copied to clipboard

Challenge: Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents.
Approach: They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process.
Outcome: The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets .

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations