MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

Recent advancements in Text-to-SQL methods employing Large Language Models (LLMs) have demonstrated remarkable performance. Nonetheless, these approaches continue to encounter difficulties when handling extensive databases, intricate user queries, and erroneous SQL results. To tackle these challenges, we present \textsc{MAC-SQL}, a novel LLM-based multi-agent collaborative framework designed for the Text-to-SQL task. Our framework comprises three agents: the \textit{Selector}, accountable for condensing voluminous databases and preserving relevant table schemas for user questions; the \textit{Decomposer}, which disassembles complex user questions into more straightforward sub-problems and resolves them progressively; and the \textit{Refiner}, tasked with validating and refining defective SQL queries. We perform comprehensive experiments on two Text-to-SQL datasets, BIRD and Spider, achieving a state-of-the-art execution accuracy of 59.59\% on the BIRD test set. Moreover, we have open-sourced an instruction fine-tuning model, SQL-Llama, based on Code Llama 7B, in addition to an agent instruction dataset derived from training data based on BIRD and Spider. The SQL-Llama model has demonstrated encouraging results on the development sets of both BIRD and Spider. However, when compared to GPT-4, there remains a notable potential for enhancement. Our code and data are publicly available at https://github.com/wbbeyourself/MAC-SQL.

QURG: 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 are few attempts to explicitly address the dependencies between current question and question context. This paper presents QURG, a novel Question Rewriting Guided approach to help the models achieve adequate contextual understanding. Specifically, we first train a question rewriting model to complete the current question based on question context, and convert them into a rewriting edit matrix. We further design a two-stream matrix encoder to jointly model the rewriting relations between question and context, and the schema linking relations between natural language and structured schema. Experimental results show that QURG significantly improves the performances on two large-scale context-dependent datasets SParC and CoSQL, especially for hard and long-turn questions.

CQR-SQL: Conversational Question Reformulation Enhanced Context-Dependent Text-to-SQL Parsers

Context-dependent text-to-SQL is the task of translating multi-turn questions into database-related SQL queries. Existing methods typically focus on making full use of history context or previously predicted SQL for currently SQL parsing, while neglecting to explicitly comprehend the schema and conversational dependency, such as co-reference, ellipsis and user focus change. In this paper, we propose CQR-SQL, which uses auxiliary Conversational Question Reformulation (CQR) learning to explicitly exploit schema and decouple contextual dependency for SQL parsing. Specifically, we first present a schema enhanced recursive CQR method to produce domain-relevant self-contained questions. Secondly, we train CQR-SQL models to map the semantics of multi-turn questions and auxiliary self-contained questions into the same latent space through schema grounding consistency task and tree-structured SQL parsing consistency task, which enhances the abilities of SQL parsing by adequately contextual understanding. At the time of writing, our CQR-SQL achieves new state-of-the-art results on two context-dependent text-to-SQL benchmarks SParC and CoSQL.

Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning

In multi-label text classification (MLTC), each given document is associated with a set of correlated labels. To capture label correlations, previous classifier-chain and sequence-to-sequence models transform MLTC to a sequence prediction task. However, they tend to suffer from label order dependency, label combination over-fitting and error propagation problems. To address these problems, we introduce a novel approach with multi-task learning to enhance label correlation feedback. We first utilize a joint embedding (JE) mechanism to obtain the text and label representation simultaneously. In MLTC task, a document-label cross attention (CA) mechanism is adopted to generate a more discriminative document representation. Furthermore, we propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning: 1) Pairwise Label Co-occurrence Prediction (PLCP), and 2) Conditional Label Co-occurrence Prediction (CLCP). Experimental results on AAPD and RCV1-V2 datasets show that our method outperforms competitive baselines by a large margin. We analyze low-frequency label performance, label dependency, label combination diversity and coverage speed to show the effectiveness of our proposed method on label correlation learning.