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20242026
most citedWhat's Wrong with Your Code Generated by Large Language Models? An Extensive Study

6 citations · 12 across the 10 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025

Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following

Qingyu Ren, Qianyu He, Powei Chang +5

Language models often struggle to follow multi-constraint instructions that are crucial for real-world applications. Existing reinforcement learning (RL) approaches suffer from dep…

cs.CL2025

A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models

Jinyi Han, Xinyi Wang, Haiquan Zhao +9

Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most exi…

cs.CL2025

Order Matters: Investigate the Position Bias in Multi-constraint Instruction Following

Jie Zeng, Qianyu He, Qingyu Ren +5

Real-world instructions with multiple constraints pose a significant challenge to existing large language models (LLMs). An observation is that the LLMs exhibit dramatic performanc…

cs.CL2025

Order Doesn't Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation

Qianxi He, Qianyu He, Jiaqing Liang +4

Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inference. However, LLMs struggle with reasoning order variations and fail to genera…

cs.CL2025

Step-by-Step Mastery: Enhancing Soft Constraint Following Ability of Large Language Models

Qingyu Ren, Jie Zeng, Qianyu He +5

It is crucial for large language models (LLMs) to follow instructions that involve multiple constraints. However, it is an unexplored area to enhance LLMs' ability to follow soft c…

cs.CL2024

Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation

Sirui Xia, Xintao Wang, Jiaqing Liang +5

Retrieval-Augmented Generation (RAG) has been widely adopted to enhance Large Language Models (LLMs) in knowledge-intensive tasks. To enhance credibility and verifiability in RAG s…

cs.CL2024★ 6 cited

EasyJailbreak: A Unified Framework for Jailbreaking Large Language Models

Weikang Zhou, Xiao Wang, Limao Xiong +18

Jailbreak attacks are crucial for identifying and mitigating the security vulnerabilities of Large Language Models (LLMs). They are designed to bypass safeguards and elicit prohibi…