5 citations · 16 across the 10 of their papers we have counts for
16 papers
Standards-Guided and Dependency-Aware Code-Level Documentation Generation
Tingxu Han, Wei Song, Ziming Li +6
Automatically generating high-quality documentation for software repositories remains challenging: existing approaches largely treat documentation as unconstrained text generation,…
Provisioning Robust and Efficient LiDAR Perception for Autonomous Driving
Tingxu Han, Zijie Shen, An Guo +4
LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency and memory r…
How Do Agentic LLMs Decide to Call Tools? A Tool-Call Vector Shaped by Suppression
Xijie Gong, Tingxu Han, Jiahao Zhang +5
Tool calling, invoking external tools on demand, is central to agentic LLMs, yet the mechanism that decides whether a model calls a tool or responds directly remains poorly underst…
Enhancing and Reporting Robustness Boundary of Neural Code Models for Intelligent Code Understanding
Tingxu Han, Wei Song, Weisong Sun +6
With the development of deep learning, Neural Code Models (NCMs) such as CodeBERT and CodeLlama are widely used for code understanding tasks, including defect detection and code cl…
SWE-Skills-Bench: Do Agent Skills Actually Help in Real-World Software Engineering?
Tingxu Han, Yi Zhang, Wei Song +4
Agent skills, structured procedural knowledge packages injected at inference time, are increasingly used to augment LLM agents on software engineering tasks. However, their real ut…
FAIRT2V: Training-Free Debiasing for Text-to-Video Diffusion Models
Haonan Zhong, Wei Song, Tingxu Han +3
Text-to-video (T2V) diffusion models have achieved rapid progress, yet their demographic biases, particularly gender bias, remain largely unexplored. We present FairT2V, a training…
Debiasing LLMs by Masking Unfairness-Driving Attention Heads
Tingxu Han, Wei Song, Ziqi Ding +6
Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear…
Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking
Ting Han, Linara Adilova, Henning Petzka +2
Neural collapse, i.e., the emergence of highly symmetric, class-wise clustered representations, is frequently observed in deep networks and is often assumed to reflect or enable ge…
Continuous Concepts Removal in Text-to-image Diffusion Models
Tingxu Han, Weisong Sun, Yanrong Hu +6
Text-to-image diffusion models have shown an impressive ability to generate high-quality images from input textual descriptions. However, concerns have been raised about the potent…
Token-Budget-Aware LLM Reasoning
Tingxu Han, Zhenting Wang, Chunrong Fang +3
Reasoning is critical for large language models (LLMs) to excel in a wide range of tasks. While methods like Chain-of-Thought (CoT) reasoning and enhance LLM performance by decompo…
Security of Language Models for Code: A Systematic Literature Review
Yuchen Chen, Weisong Sun, Chunrong Fang +7
Language models for code (CodeLMs) have emerged as powerful tools for code-related tasks, outperforming traditional methods and standard machine learning approaches. However, these…
Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders
Tingxu Han, Weisong Sun, Ziqi Ding +5
Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can…
Invisible Backdoor Attack against Self-supervised Learning
Hanrong Zhang, Zhenting Wang, Boheng Li +7
Self-supervised learning (SSL) models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in SSL often involve noticeable triggers, like colored patche…
Can Distillation Mitigate Backdoor Attacks in Pre-trained Encoders?
TIngxu Han, Wei Song, Weisong Sun +7
Self-Supervised Learning (SSL) has become a prominent paradigm for pre-training encoders to learning general-purpose representations from unlabeled data and releasing them on third…
Adaptive loose optimization for robust question answering
Jie Ma, Pinghui Wang, Zewei Wang +4
Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (…
An Extractive-and-Abstractive Framework for Source Code Summarization
Weisong Sun, Chunrong Fang, Yuchen Chen +6
(Source) Code summarization aims to automatically generate summaries/comments for a given code snippet in the form of natural language. Such summaries play a key role in helping de…