14 citations · 79 across the 19 of their papers we have counts for
25 papers
Make Your LLM Fully Utilize the Context
Shengnan An, Zexiong Ma, Zeqi Lin +2
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-m…
Learning From Mistakes Makes LLM Better Reasoner
Shengnan An, Zexiong Ma, Zeqi Lin +3
Large language models (LLMs) recently exhibited remarkable reasoning capabilities on solving math problems. To further improve their reasoning capabilities, this work explores whet…
How Do In-Context Examples Affect Compositional Generalization?
Shengnan An, Zeqi Lin, Qiang Fu +4
Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence. The AI community mainly studies this…
Uncovering and Categorizing Social Biases in Text-to-SQL
Yan Liu, Yan Gao, Zhe Su +3
Content Warning: This work contains examples that potentially implicate stereotypes, associations, and other harms that could be offensive to individuals in certain social groups.}…
Uncovering and Quantifying Social Biases in Code Generation
Yan Liu, Xiaokang Chen, Yan Gao +6
With the popularity of automatic code generation tools, such as Copilot, the study of the potential hazards of these tools is gaining importance. In this work, we explore the socia…
TACR: A Table-alignment-based Cell-selection and Reasoning Model for Hybrid Question-Answering
Jian Wu, Yicheng Xu, Yan Gao +3
Hybrid Question-Answering (HQA), which targets reasoning over tables and passages linked from table cells, has witnessed significant research in recent years. A common challenge in…