Generating accurate SQL from natural language questions (text-to-SQL) is a long-standing
challenge due to the complexities in user question understanding, database schema …

Translating users' natural language queries (NL) into SQL queries (ie, NL2SQL) can
significantly reduce barriers to accessing relational databases and support various …

Text-to-SQL parsing, which aims at converting natural language instructions into executable
SQLs, has gained increasing attention in recent years. In particular, GPT-4 and Claude-2 …

Generating accurate SQL queries for user questions (text-to-SQL) has been a long-standing
challenge since it requires a deep understanding of both the user's question and the …

The emergence of natural language processing has revolutionized the way users interact
with tabular data, enabling a shift from traditional query languages and manual plotting to …

Table reasoning aims to generate inference results based on the user requirement and the
provided table. Enhancing the table reasoning capability of the model can aid in obtaining …

We present Archer, a challenging bilingual text-to-SQL dataset specific to complex
reasoning, including arithmetic, commonsense and hypothetical reasoning. It contains 1,042 …

The task of text-to-SQL aims to convert a natural language question into its corresponding
SQL query within the context of relational tables. Existing text-to-SQL parsers generate a …

Humans possess a remarkable ability to assign novel interpretations to linguistic
expressions, enabling them to learn new words and understand community-specific …

Data scarcity is a prevalent challenge in the era of Large Language Models (LLMs). The
insatiable hunger of LLMs for large corpora becomes even more pronounced when dealing …