11 citations · 28 across the 15 of their papers we have counts for
10 papers · 1 filter
Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format
Dingzirui Wang, Xuanliang Zhang, Rongyu Cao +8
Generating and voting multiple answers is an effective method to mitigate reasoning inconsistencies of large language models (LLMs). Prior works have shown that multiple reasoning…
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…
EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations
Jia Li, Ge Li, Xuanming Zhang +6
How to evaluate Large Language Models (LLMs) in code generation remains an open question. Existing benchmarks have two limitations - data leakage and lack of domain-specific evalua…
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Zefeng Cai, Xiangyu Li, Binyuan Hui +8
In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural languag…
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
Bowen Qin, Lihan Wang, Binyuan Hui +7
This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncert…
Towards Generalizable and Robust Text-to-SQL Parsing
Chang Gao, Bowen Li, Wenxuan Zhang +5
Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries. In practice, text-to-SQL parsers often encounter various challenging scenar…