1 citations · 1 across the 4 of their papers we have counts for
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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…
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…
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…
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…
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…
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…