Papers SQL Parsing
“SQL Parsing” 태그가 달린 논문 50편 · 필터 해제
Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies
While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building trustworthy text-to-SQL systems thus requ…
SQL ParsingText to SQLText-To-SQLKeyInst: Keyword Instruction for Improving SQL Formulation in Text-to-SQL
Text-to-SQL parsing involves the translation of natural language queries (NLQs) into their corresponding SQL commands. A principal challenge within this domain is the formulation of SQL queries that are not only syntacti…
Natural Language QueriesSQL ParsingText to SQLText-To-SQLSynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA
Text-to-SQL parsing and end-to-end question answering (E2E TQA) are two main approaches for Table-based Question Answering task. Despite success on multiple benchmarks, they have yet to be compared and their synergy rema…
Answer SelectionQuestion AnsweringSemantic ParsingSQL Parsing+3Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models
This study explores text-to-SQL parsing by leveraging the powerful reasoning capabilities of large language models (LLMs). Despite recent advancements, existing LLM-based methods have not adequately addressed scalability…
Information RetrievalRetrievalSQL ParsingText to SQL+1A Survey on Employing Large Language Models for Text-to-SQL Tasks
The increasing volume of data in relational databases and the expertise needed for writing SQL queries pose challenges for users to access and analyze data. Text-to-SQL (Text2SQL) solves the issues by utilizing natural l…
Natural Language QueriesPrompt EngineeringSQL ParsingText to SQL+1SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaboration
While fine-tuned large language models (LLMs) excel in generating grammatically valid SQL in Text-to-SQL parsing, they often struggle to ensure semantic accuracy in queries, leading to user confusion and diminished syste…
SQL ParsingText to SQLText-To-SQLvalidEHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records
In this paper, we introduce EHR-SeqSQL, a novel sequential text-to-SQL dataset for Electronic Health Record (EHR) databases. EHR-SeqSQL is designed to address critical yet underexplored aspects in text-to-SQL parsing: in…
SQL ParsingText to SQLText-To-SQLTowards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey
Compositional generalization is the ability of a model to generalize to complex, previously unseen types of combinations of entities from just having seen the primitives. This type of generalization is particularly relev…
Information RetrievalRetrievalSemantic ParsingSQL Parsing+2When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
In this paper, we examine how large language models (LLMs) solve multi-step problems under a language agent framework with three components: a generator, a discriminator, and a planning method. We investigate the practic…
Mathematical ReasoningRe-RankingSQL ParsingText to SQL+1MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
Recent LLM-based Text-to-SQL methods usually suffer from significant performance degradation on "huge" databases and complex user questions that require multi-step reasoning. Moreover, most existing methods neglect the c…
SQL ParsingText to SQLText-To-SQLSemantic Decomposition of Question and SQL for Text-to-SQL Parsing
Text-to-SQL semantic parsing faces challenges in generalizing to cross-domain and complex queries. Recent research has employed a question decomposition strategy to enhance the parsing of complex SQL queries. However, th…
RetrievalSemantic ParsingSQL ParsingText to SQL+1Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison
The success of ChatGPT has ignited an AI race, with researchers striving to develop new large language models (LLMs) that can match or surpass the language understanding and generation abilities of commercial ones. In re…
SQL ParsingText to SQLText-To-SQLExploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing
In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each interaction can be viewed as component mod…
SQL ParsingText to SQLText-To-SQLExploring Chain-of-Thought Style Prompting for Text-to-SQL
In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much roo…
In-Context LearningSQL ParsingText to SQLText-To-SQLText-to-SQL Error Correction with Language Models of Code
Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing…
SQL ParsingText to SQLText-To-SQLWav2SQL: Direct Generalizable Speech-To-SQL Parsing
Speech-to-SQL (S2SQL) aims to convert spoken questions into SQL queries given relational databases, which has been traditionally implemented in a cascaded manner while facing the following challenges: 1) model training i…
SQL ParsingQURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing
Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implicitly for contextual SQL parsing, there a…
Question RewritingSemantic ParsingSQL ParsingText to SQL+1Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this ta…
Semantic ParsingSQL ParsingText to SQLText-To-SQLRESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tabl…
DecoderLanguage ModelingLanguage ModellingSemantic Parsing+3Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing
The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years, as it can assist end users in efficiently extracting vi…
Domain GeneralizationInductive BiasSemantic ParsingSQL Parsing+2