12 citations · 39 across the 53 of their papers we have counts for
57 papers
SFAD: Speculative Factuality-Aware Decoding
Guanqiao Chen, Di Wang, Lijie Hu
As one of the most critical challenges in large language models, contextual faithfulness directly determines their reliability in knowledge-intensive applications. This task is par…
Algorithmic Recourse of In-Context Learning for Tabular Data
Wenshuo Dong, Jiaming Zhang, Shaopeng Fu +3
As predictive models are increasingly deployed in high-stakes settings such as credit approval, there is a growing need for post-hoc methods that provide recourse to affected indiv…
Predicting LLM Output Length via Entropy-Guided Representations
Huanyi Xie, Yubin Chen, Liangyu Wang +2
The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched…
Concept-Based Dictionary Learning for Inference-Time Safety in Vision Language Action Models
Siqi Wen, Shu Yang, Shaopeng Fu +3
Vision Language Action (VLA) models close the perception action loop by translating multimodal instructions into executable behaviors, but this very capability magnifies safety ris…
Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency
Xinyan Jiang, Wenjing Yu, Di Wang +1
Activation engineering enables precise control over Large Language Models (LLMs) without the computational cost of fine-tuning. However, existing methods deriving vectors from stat…
Understanding the Dynamics of Demonstration Conflict in In-Context Learning
Difan Jiao, Di Wang, Lijie Hu
In-context learning enables large language models to perform novel tasks through few-shot demonstrations. However, demonstrations per se can naturally contain noise and conflicting…
Beyond Scalars: Evaluating and Understanding LLM Reasoning via Geometric Progress and Stability
Xinyan Jiang, Ninghao Liu, Di Wang +1
Evaluating LLM reliability via scalar probabilities often fails to capture the structural dynamics of reasoning. We introduce TRACED, a framework that assesses reasoning quality th…
Dual-Kernel Adapter: Expanding Spatial Horizons for Data-Constrained Medical Image Analysis
Ziquan Zhu, Hanruo Zhu, Siyuan Lu +8
Adapters have become a widely adopted strategy for efficient fine-tuning of large pretrained models, particularly in resource-constrained settings. However, their performance under…
In-Run Data Shapley for Adam Optimizer
Meng Ding, Zeqing Zhang, Di Wang +1
Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold stand…
Controlling Repetition in Protein Language Models
Jiahao Zhang, Zeqing Zhang, Di Wang +1
Protein language models (PLMs) have enabled advances in structure prediction and de novo protein design, yet they frequently collapse into pathological repetition during generation…
Hearing is Believing? Evaluating and Analyzing Audio Language Model Sycophancy with SYAUDIO
Junchi Yao, Lokranjan Lakshmikanthan, Annie Zhao +5
Audio Language Models (ALMs) have recently shown strong capabilities in unified reasoning over speech, sound, and natural language; yet we find that they can inherit sycophancy, th…
Controllable Concept Bottleneck Models
Hongbin Lin, Chenyang Ren, Juangui Xu +7
Concept Bottleneck Models (CBMs) have garnered much attention for their ability to elucidate the prediction process through a human-understandable concept layer. However, most prev…
Towards Reasoning-Preserving Unlearning in Multimodal Large Language Models
Hongji Li, Junchi yao, Manjiang Yu +4
Machine unlearning aims to erase requested data from trained models without full retraining. For Reasoning Multimodal Large Language Models (RMLLMs), this is uniquely challenging:…
PIXEL: Adaptive Steering Via Position-wise Injection with eXact Estimated Levels under Subspace Calibration
Manjiang Yu, Hongji Li, Priyanka Singh +3
Reliable behavior control is central to deploying large language models (LLMs) on the web. Activation steering offers a tuning-free route to align attributes (e.g., truthfulness) t…
When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs
Zhuoran Zhang, Tengyue Wang, Xilin Gong +4
Multimodal large language models (MLLMs) must resolve conflicts when different modalities provide contradictory information, a process we term modality following. Prior work measur…
PAHQ: Accelerating Automated Circuit Discovery through Mixed-Precision Inference Optimization
Xinhai Wang, Shu Yang, Liangyu Wang +4
Circuit discovery, which involves identifying sparse and task-relevant subnetworks in pre-trained language models, is a cornerstone of mechanistic interpretability. Automated Circu…
Benchmarking and Mitigating Sycophancy in Medical Vision Language Models
Juangui Xu, Zikun Guo, Jingwei Lv +5
Visual language models (VLMs) have the potential to transform medical workflows. However, the deployment is limited by sycophancy. Despite this serious threat to patient safety, a…
Attributing Data for Sharpness-Aware Minimization
Chenyang Ren, Yifan Jia, Huanyi Xie +5
Sharpness-aware Minimization (SAM) improves generalization in large-scale model training by linking loss landscape geometry to generalization. However, challenges such as mislabele…
The Compositional Architecture of Regret in Large Language Models
Xiangxiang Cui, Shu Yang, Tianjin Huang +3
Regret in Large Language Models refers to their explicit regret expression when presented with evidence contradicting their previously generated misinformation. Studying the regret…
Mitigating Behavioral Hallucination in Multimodal Large Language Models for Sequential Images
Liangliang You, Junchi Yao, Shu Yang +3
While multimodal large language models excel at various tasks, they still suffer from hallucinations, which limit their reliability and scalability for broader domain applications.…
Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment
Huanyi Xie, Lijie Hu, Lu Yu +6
In the realm of Text-attributed Graphs (TAGs), traditional graph neural networks (GNNs) often fall short due to the complex textual information associated with each node. Recent me…
Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Wenshuo Dong, Qingsong Yang, Shu Yang +5
Large Language Models (LLMs) trained on massive data capture rich information embedded in the training data. However, this also introduces the risk of privacy leakage, particularly…
Stable Vision Concept Transformers for Medical Diagnosis
Lijie Hu, Songning Lai, Yuan Hua +3
Transparency is a paramount concern in the medical field, prompting researchers to delve into the realm of explainable AI (XAI). Among these XAI methods, Concept Bottleneck Models…
COMPKE: Complex Question Answering under Knowledge Editing
Keyuan Cheng, Zijian Kan, Zhixian He +5
Knowledge Editing, which efficiently modifies the knowledge in large language models, has gathered great attention. Current benchmarks primarily use multi-hop question answering to…