90 citations · 397 across the 44 of their papers we have counts for
44 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…
Question Answering as Programming for Solving Time-Sensitive Questions
Xinyu Zhu, Cheng Yang, Bei Chen +3
Question answering plays a pivotal role in human daily life because it involves our acquisition of knowledge about the world. However, due to the dynamic and ever-changing nature o…
Skill-Based Few-Shot Selection for In-Context Learning
Shengnan An, Bo Zhou, Zeqi Lin +5
In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each…
Explainable Automated Debugging via Large Language Model-driven Scientific Debugging
Sungmin Kang, Bei Chen, Shin Yoo +1
Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with exist…
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
Fengji Zhang, Bei Chen, Yue Zhang +6
The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository. While for automated code completion tools, it…
LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models
Junyi Zhang, Jiaqi Guo, Shizhao Sun +2
Creating graphic layouts is a fundamental step in graphic designs. In this work, we present a novel generative model named LayoutDiffusion for automatic layout generation. As layou…
Does Deep Learning Learn to Abstract? A Systematic Probing Framework
Shengnan An, Zeqi Lin, Bei Chen +3
Abstraction is a desirable capability for deep learning models, which means to induce abstract concepts from concrete instances and flexibly apply them beyond the learning context.…
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge
Longxu Dou, Yan Gao, Xuqi Liu +6
In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific table…
MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing
Longxu Dou, Yan Gao, Mingyang Pan +4
Text-to-SQL semantic parsing is an important NLP task, which greatly facilitates the interaction between users and the database and becomes the key component in many human-computer…
Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation
Xinyu Pi, Bing Wang, Yan Gao +3
The robustness of Text-to-SQL parsers against adversarial perturbations plays a crucial role in delivering highly reliable applications. Previous studies along this line primarily…
Large Language Models Meet NL2Code: A Survey
Daoguang Zan, Bei Chen, Fengji Zhang +5
The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence. Thanks to the rapid developmen…
Know What I don't Know: Handling Ambiguous and Unanswerable Questions for Text-to-SQL
Bing Wang, Yan Gao, Zhoujun Li +1
The task of text-to-SQL aims to convert a natural language question into its corresponding SQL query within the context of relational tables. Existing text-to-SQL parsers generate…
When Language Model Meets Private Library
Daoguang Zan, Bei Chen, Zeqi Lin +3
With the rapid development of pre-training techniques, a number of language models have been pre-trained on large-scale code corpora and perform well in code generation. In this pa…
CodeT: Code Generation with Generated Tests
Bei Chen, Fengji Zhang, Anh Nguyen +4
The task of generating code solutions for a given programming problem can benefit from the use of pre-trained language models such as Codex, which can produce multiple diverse samp…
CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation
Daoguang Zan, Bei Chen, Dejian Yang +6
Code generation is a longstanding challenge, aiming to generate a code snippet based on a natural language description. Usually, expensive text-code paired data is essential for tr…
Making Large Language Models Better Reasoners with Step-Aware Verifier
Yifei Li, Zeqi Lin, Shizhuo Zhang +4
Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress i…
DFM: Dialogue Foundation Model for Universal Large-Scale Dialogue-Oriented Task Learning
Zhi Chen, Jijia Bao, Lu Chen +10
Building a universal conversational agent has been a long-standing goal of the dialogue research community. Most previous works only focus on a small set of dialogue tasks. In this…
LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
Xinyu Pi, Wanjun Zhong, Yan Gao +2
We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Upon automatic identifying logical reasoning ph…
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual Spoken Language Understanding
Libo Qin, Qiguang Chen, Tianbao Xie +4
Due to high data demands of current methods, attention to zero-shot cross-lingual spoken language understanding (SLU) has grown, as such approaches greatly reduce human annotation…