most citedA Survey of Table Reasoning with Large Language Models

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cs.CL2024

Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits

Bohan Li, Jiannan Guan, Longxu Dou +12

The Myers-Briggs Type Indicator (MBTI) is one of the most influential personality theories reflecting individual differences in thinking, feeling, and behaving. MBTI personality de…

cs.CL2024

SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types

Xuanliang Zhang, Dingzirui Wang, Baoxin Wang +6

Scientific question answering (SQA) is an important task aimed at answering questions based on papers. However, current SQA datasets have limited reasoning types and neglect the re…

cs.CL2024

In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks

Dingzirui Wang, Xuanliang Zhang, Qiguang Chen +9

In-context learning (ICL) is an effective approach to help large language models (LLMs) adapt to various tasks by providing demonstrations of the target task. Considering the high…

cs.CL2024

FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats

Xuanliang Zhang, Dingzirui Wang, Longxu Dou +4

The table reasoning task aims to answer the question according to the given table. Currently, using Large Language Models (LLMs) is the predominant method for table reasoning. Most…

cs.CL20243 cited

DAC: Decomposed Automation Correction for Text-to-SQL

Dingzirui Wang, Longxu Dou, Xuanliang Zhang +2

Text-to-SQL is an important task that helps people obtain information from databases by automatically generating SQL queries. Considering the brilliant performance, approaches base…

cs.CL2024

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes

Dingzirui Wang, Longxu Dou, Xuanliang Zhang +2

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 reason…