7 citations · 15 across the 28 of their papers we have counts for
29 papers
FormalTCS: Benchmarking End-to-End Frontier Formal Theoretical Computer Science Research of Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language models (LLMs) have shown growing potential for automated theoretical computer science (TCS) research, yet existing benchmarks remain far from realistic research sett…
SDAM: Structure-Difference-Aware Memory Evolution for Complex Text-to-SQL
Keyan Xu, Dingzirui Wang, Xuanliang Zhang +2
Text-to-SQL aims to convert natural language questions into executable SQL queries. While memory-based agent system improves complex SQL generation, existing memory design neglect…
CurateEvo: Data-Curation Evolving for Agentic Post-Training
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2
Large language model (LLM) agents require post-training methods that can improve long-horizon decision making from environment feedback. However, existing agentic post-training pip…
Scaling Laws for Agent Harnesses via Effective Feedback Compute
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
Agent harnesses shape language-model performance by controlling tool use, feedback, verification, memory, and repair. Yet raw test-time expenditure, such as tokens, tool calls, wal…
Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization
Zhihao Liu, Yifan Wu, Jian Lou +3
Safety alignment for large language models (LLMs) aims to reduce harmful or unsafe behavior while preserving general utility. However, recent findings reveal that alignment effects…
How Do Language Models Understand Tables? A Mechanistic Analysis of Cell Location
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
While Large Language Models (LLMs) are increasingly deployed for table-related tasks, the internal mechanisms enabling them to process linearized two-dimensional structured tables…
When Does Context Help? Error Dynamics of Contextual Information in Large Language Models
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Contextual information at inference time, such as demonstrations, retrieved knowledge, or interaction history, can substantially improve large language models (LLMs) without parame…
Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Existing research indicates that the output of Chain-of-Thought (CoT) is significantly affected by input perturbations. Although many methods aim to mitigate such impact by optimiz…
Multi-Layer Attention is the Amplifier of Demonstration Effectiveness
Dingzirui Wang, Xuangliang Zhang, Keyan Xu +3
Numerous studies have investigated the underlying mechanisms of in-context learning (ICL) effectiveness to inspire the design of related methods. However, existing work predominant…
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…
AlignEvoSkill: Towards Knowledge-Aware and Task-Aligned Agent Skill Evolution
Dingzirui Wang, Xuanliang Zhang, Keyan Xu +3
Reusable skills play a key role in improving LLM-based agents, but existing skill-evolution methods often fail to ensure that evolved skills both cover the knowledge required by th…
RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
The table reasoning task, crucial for efficient data acquisition, aims to answer questions based on the given table. Recently, reasoning large language models (RLLMs) with Long Cha…
Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval
Keyan Xu, Dingzirui Wang, Xuanliang Zhang +2
The existing text-to-SQL systems have made significant progress in SQL query generation, but they still face numerous challenges. Existing systems often lack retrieval capabilities…
MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
Xuanliang Zhang, Dingzirui Wang, Keyan Xu +2
Question answering on the hybrid context of tables and text (TATQA) is a critical task, with broad applications in data-intensive domains. However, existing TATQA datasets are limi…
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…
A Survey of Table Reasoning with Large Language Models
Xuanliang Zhang, Dingzirui Wang, Longxu Dou +2
Table reasoning, which aims to generate the corresponding answer to the question following the user requirement according to the provided table, and optionally a text description o…
MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
Xuanliang Zhang, Dingzirui Wang, Longxu Dou +2
The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive databases and generate SQL. However, the performance of current methods is constrained by si…
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL
Dingzirui Wang, Longxu Dou, Xuanliang Zhang +2
Currently, the in-context learning method based on large language models (LLMs) has become the mainstream of text-to-SQL research. Previous works have discussed how to select demon…
Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning
Dingzirui Wang, Longxu Dou, Wenbin Zhang +2
Numerical reasoning is vital for natural language processing models to understand and process numerical information in real-world scenarios. Most current methods first generate the…