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TIR - Research Initiation Project: Datasets, Metrics, PLMs, and LLMs for Text-to-SQL

Course Overview

Why TIR?

The 'Travaux d'Initiation à la Recherche' (TIR) is a fundamental component of the Computer Science curriculum designed to immerse students in the scientific approach and critical thinking essential in research. This module is not intended to make you a researcher but to provide a glimpse into what computer science research entails.

Objectives

Work: Introduction to scientific work on a specific topic. Initiation: A stepping stone into the world of computer science research. Research: Encouragement of critical thinking and continuous skill development.

Course Structure

Common to all CS specialities, addressing research generically. Focus on enhancing critical thinking, an essential skill in professional careers. Teamwork under the guidance of a researcher or client. Completion across three main stages: Literature Search, Full State of the Art, and Setting the Scene.

Project Topic: Datasets, Metrics, PLMs, and LLMs for Text-to-SQL.

Our group's focus is on exploring the intersection of Pre-trained Language Models (PLMs), Large Language Models (LLMs), and their application in Text-to-SQL contexts. We aim to investigate various datasets and metrics relevant to this field.

Research Papers

We commence our exploration with a set of key research papers. These are housed in our docs subfolder for easy reference:

Improving Text-to-SQL Evaluation Methodology

KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers

Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs

Stage Progression

Stage 1: Literature Search and State of the Art

Objective: To develop a comprehensive understanding of the current research landscape. Output: Bibliography files documenting relevant literature.

Stage 2: Full State of the Art

Objective: To delve deeper into the literature and develop a nuanced understanding. Output: A detailed written document outlining our findings.

Stage 3: Setting the Scene

Objective: To synthesize our learning and present our insights. Output: Peer-reviewed content and a poster presentation.

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