activity
20232026
most citedTest-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations

3 citations · 8 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting

Cheng Wang, Qin Liu, Wenxuan Zhou +1

Group Relative Policy Optimization (GRPO) has emerged as a promising approach for improving the reasoning capabilities of large language models. However, it struggles to effectivel…

cs.CL2026

DebugLM: Learning Traceable Training Data Provenance for LLMs

Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +3

Large language models (LLMs) are trained through multi-stage pipelines over heterogeneous data sources, yet developers lack a principled way to pinpoint the specific data responsib…

cs.CV2025★ 1 cited

FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models

Jiyoon Pyo, Yuankun Jiao, Dongwon Jung +11

Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map i…

cs.CL2025

False Sense of Security: Why Probing-based Malicious Input Detection Fails to Generalize

Cheng Wang, Zeming Wei, Qin Liu +1

Large Language Models (LLMs) can comply with harmful instructions, raising serious safety concerns despite their impressive capabilities. Recent work has leveraged probing-based ap…

cs.SE2025★ 1 cited

RedCoder: Automated Multi-Turn Red Teaming for Code LLMs

Wenjie Jacky Mo, Qin Liu, Xiaofei Wen +5

Large Language Models (LLMs) for code generation (i.e., Code LLMs) have demonstrated impressive capabilities in AI-assisted software development and testing. However, recent studie…

cs.CL2025★ 3 cited

QA-LIGN: Aligning LLMs through Constitutionally Decomposed QA

Jacob Dineen, Aswin RRV, Qin Liu +8

Alignment of large language models (LLMs) with principles like helpfulness, honesty, and harmlessness typically relies on scalar rewards that obscure which objectives drive the tra…

cs.CL2024

Securing Multi-turn Conversational Language Models From Distributed Backdoor Triggers

Terry Tong, Jiashu Xu, Qin Liu +1

Large language models (LLMs) have acquired the ability to handle longer context lengths and understand nuances in text, expanding their dialogue capabilities beyond a single uttera…

cs.CL2023★ 3 cited

Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations

Wenjie Mo, Jiashu Xu, Qin Liu +5

Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense. This gap becomes pronounced in the c…