9 papers
Look Clearly Before Answering: Mitigating Hallucinations in LVLMs via Saliency-Driven Perceptual Realignment
Pengxu Chen, Yao Zhu, Guangming Zhu +4
Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding. However, they remain prone to hallucinations, generating responses that…
Beyond Scaling: Assessing Strategic Reasoning and Rapid Decision-Making Capability of LLMs in Zero-sum Environments
Yang Li, Xing Chen, Yutao Liu +5
Large Language Models (LLMs) have achieved strong performance on static reasoning benchmarks, yet their effectiveness as interactive agents operating in adversarial, time-sensitive…
Pruning as a Cooperative Game: Surrogate-Assisted Layer Contribution Estimation for Large Language Models
Xuan Ding, Pengyu Tong, Ranjie Duan +3
While large language models (LLMs) demonstrate impressive performance across various tasks, their deployment in real-world scenarios is still constrained by high computational dema…
Resource-Efficient Reinforcement for Reasoning Large Language Models via Dynamic One-Shot Policy Refinement
Yunjian Zhang, Sudong Wang, Yang Li +5
Large language models (LLMs) have exhibited remarkable performance on complex reasoning tasks, with reinforcement learning under verifiable rewards (RLVR) emerging as a principled…
SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition
Peiran Xu, Sudong Wang, Yao Zhu +3
Spatial cognition is fundamental to real-world multimodal intelligence, allowing models to effectively interact with the physical environment. While multimodal large language model…
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression
Xuan Ding, Rui Sun, Yunjian Zhang +6
The ever-increasing computational demands and deployment costs of large language models (LLMs) have spurred numerous compressing methods. Compared to quantization and unstructured…
Towards Understanding How Knowledge Evolves in Large Vision-Language Models
Sudong Wang, Yunjian Zhang, Yao Zhu +4
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications. However, understanding their internal working mechanisms h…
Sliding-Window Merging for Compacting Patch-Redundant Layers in LLMs
Xuan Ding, Rui Sun, Yunjian Zhang +7
Depth-wise pruning accelerates LLM inference in resource-constrained scenarios but suffers from performance degradation due to direct removal of entire Transformer layers. This pap…
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction
Yinhan He, Zaiyi Zheng, Patrick Soga +3
In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis. However, due to the black-box nature of GNNs,…