7 papers
Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
Xingwei Qu, Shaowen Wang, Zihao Huang +16
Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity…
CEMG: Collaborative-Enhanced Multimodal Generative Recommendation
Yuzhen Lin, Hongyi Chen, Xuanjing Chen +3
Generative recommendation models often struggle with two key challenges: (1) the superficial integration of collaborative signals, and (2) the decoupled fusion of multimodal featur…
Adaptive Cache Pollution Control for Large Language Model Inference Workloads Using Temporal CNN-Based Prediction and Priority-Aware Replacement
Songze Liu, Hongkun Du, Shaowen Wang
Large Language Models (LLMs), such as GPT and LLaMA, introduce unique memory access characteristics during inference due to frequent token sequence lookups and embedding vector ret…
When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs
Shaowen Wang, Yiqi Dong, Ruinian Chang +4
Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this paper, we highlight a critical…
PLUM: Adapting Pre-trained Language Models for Industrial-scale Generative Recommendations
Ruining He, Lukasz Heldt, Lichan Hong +20
Large Language Models (LLMs) pose a new paradigm of modeling and computation for information tasks. Recommendation systems are a critical application domain poised to benefit signi…
Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
Zhixuan Pan, Shaowen Wang, Jian Li
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet principled explanations for their underlying mechanisms and several phenomena, suc…