8 citations · 18 across the 43 of their papers we have counts for
13 papers · 2 filters
How Many Code and Test Cases Are Enough? Evaluating Test Cases Generation from a Binary-Matrix Perspective
Xianzhen Luo, Jinyang Huang, Wenzhen Zheng +5
Evaluating test cases automatically generated by Large Language Models (LLMs) is a critical yet challenging task. Existing benchmarks often evaluate the exclusion ratio on large, u…
Scaling Laws for Code: A More Data-Hungry Regime
Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu +5
Code Large Language Models (LLMs) are revolutionizing software engineering. However, scaling laws that guide the efficient training are predominantly analyzed on Natural Language (…
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…
Learning-to-Context Slope: Evaluating In-Context Learning Effectiveness Beyond Performance Illusions
Dingzriui Wang, Xuanliang Zhang, Keyan Xu +3
In-context learning (ICL) has emerged as an effective approach to enhance the performance of large language models (LLMs). However, its effectiveness varies significantly across mo…
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…
Advancing Tool-Augmented Large Language Models via Meta-Verification and Reflection Learning
Zhiyuan Ma, Jiayu Liu, Xianzhen Luo +3
Empowering large language models (LLMs) with effective tool utilization capabilities is crucial for enabling AI agents to solve complex problems. However, current models face two m…
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…
Success is in the Details: Evaluate and Enhance Details Sensitivity of Code LLMs through Counterfactuals
Xianzhen Luo, Qingfu Zhu, Zhiming Zhang +8
Code Sensitivity refers to the ability of Code LLMs to recognize and respond to details changes in problem descriptions. While current code benchmarks and instruction data focus on…
Is Compression Really Linear with Code Intelligence?
Shijie Xuyang, Xianzhen Luo, Zheng Chu +6
Understanding the relationship between data compression and the capabilities of Large Language Models (LLMs) is crucial, especially in specialized domains like code intelligence. P…
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…