In-context learning with large language models (LLMs) is the current mainstream method for text-to-SQL. Previous studies have explored selecting relevant demonstrations from a human-labeled demonstration pool, but these methods lack diversity and incur high labeling costs. In this work, we address measuring and enhancing the diversity of the text-to-SQL demonstration pool. First, we introduce a diversity metric and present that the diversity of the existing labeling data can be further enhanced. Motivated by these findings, we propose Fused that iteratively fuses demonstrations to create a diverse demonstration pool based on human labeling or even from scratch with LLMs, reducing labeling costs. Fused achieves an average improvement of 2.1% based on existing labeling and 5.5% from scratch on several mainstream datasets, demonstrating its effectiveness.

Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL

Numerical reasoning is an essential ability for NLP systems to handle numeric information. Recent research indicates that fine-tuning a small-scale model to learn generating reasoning processes alongside answers can significantly enhance performance. However, current methods have the limitation that most methods generate reasoning processes with large language models (LLMs), which are "unreliable" since such processes could contain information unrelated to the answer. To address this limitation, we introduce enhancing numerical reasoning with reliable processes (Encore), which derives the reliable reasoning process by decomposing the answer formula, ensuring which fully supports the answer. Nevertheless, models could lack enough data to learn the reasoning process generation adequately, since our method generates only one single reasoning process for one formula. To overcome this difficulty, we present a series of pre-training tasks to help models learn the reasoning process generation with synthesized data. The experiments show that Encore yields improvement on all five experimental datasets with an average of 1.8%, proving the effectiveness of our method.

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes

Text-to-SQL semantic parsing is an important NLP task, which facilitates the interaction between users and the database. Much recent progress in text-to-SQL has been driven by large-scale datasets, but most of them are centered on English. In this work, we present MultiSpider, the largest multilingual text-to-SQL semantic parsing dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnam). Upon MultiSpider we further identify the lexical and structural challenges of text-to-SQL  (caused by specific language properties and dialect sayings) and their intensity across different languages. Experimental results under various settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. Qualitative and quantitative analyses are conducted to understand the reason for the performance drop of each language. Besides the dataset, we also propose a simple schema augmentation framework SAVe (Schema-Augmentation-with-Verification), which significantly boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.


MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

Numerical reasoning is a vital capability for natural language processing models to understand and process numerical information in real-world scenarios.
Most current methods first generate the Intermediate Meaning Representations (IMRs) of questions and then generate answers.
Current SOTA methods generate programs as IMRs with large language models (LLMs).
Intuitively, equations have fewer restrictions and closer semantics to the question than programs, leading to higher generation accuracy.
However, current LLMs generate equations worse than programs, where we assume that the equation data is rare in pre-training data compared to programs.
So in this paper, we try to use equations as IMRs to solve the numerical reasoning task by addressing two problems:
(1) Theoretically, how to prove that the equation is an IMR with higher generation accuracy than programs;
(2) Empirically, how to improve the generation accuracy of equations with LLMs.
For the first problem, we propose and prove a proposition to theoretically compare the generation accuracy of different IMRs.
For the second problem, we present a method called Boosting Numerical Reasoning by Decomposing the Generation of Equations (Bridge), which can improve the accuracy of LLMs in generating equations as IMRs by reducing the tendency of generating constant expressions and programs.
Our method improves the performance by 2.2%, 0.9%, and 1.7% on GSM8K, SVAMP, and Algebra datasets compared to the previous state-of-the-art methods under the single reasoning path setting.

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning of Large Language Models

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SNLP: Lexical & Frame Semantics, Semantic Parsing

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4

SHORT BIO

Presentations

Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes

MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning of Large Language Models

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