7 papers · 1 filter
The Few Govern the Many:Unveiling Few-Layer Dominance for Time Series Models
Xin Qiu, Junlong Tong, Yirong Sun +2
Large-scale models are at the forefront of time series (TS) forecasting, dominated by two paradigms: fine-tuning text-based Large Language Models (LLM4TS) and training Time Series…
: Decoding Discontinuous Cross-Modal Dynamics for Efficient Multimodal LLMs
Yingqi Fan, Anhao Zhao, Jinlan Fu +5
Multimodal Large Language Models (MLLMs) have achieved strong performance across vision-language tasks, but suffer from significant computational overhead due to the quadratic grow…
StreamingThinker: Large Language Models Can Think While Reading
Junlong Tong, Yingqi Fan, Anhao Zhao +2
Large language models (LLMs) have demonstrated remarkable capabilities in chain of thought (CoT) reasoning. However, the current LLM reasoning paradigm initiates thinking only afte…
MCM-DPO: Multifaceted Cross-Modal Direct Preference Optimization for Alt-text Generation
Jinlan Fu, Shenzhen Huangfu, Hao Fei +4
The alt-text generation task produces concise, context-relevant descriptions of images, enabling blind and low-vision users to access online images. Despite the capabilities of lar…
SkipGPT: Dynamic Layer Pruning Reinvented with Token Awareness and Module Decoupling
Anhao Zhao, Fanghua Ye, Yingqi Fan +4
Large language models (LLMs) achieve remarkable performance across tasks but incur substantial computational costs due to their deep, multi-layered architectures. Layer pruning has…
LLM as Effective Streaming Processor: Bridging Streaming-Batch Mismatches with Group Position Encoding
Junlong Tong, Jinlan Fu, Zixuan Lin +4
Large Language Models (LLMs) are primarily designed for batch processing. Existing methods for adapting LLMs to streaming rely either on expensive re-encoding or specialized archit…
CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs
Jinlan Fu, Shenzhen Huangfu, Hao Fei +4
Multimodal Large Language Models (MLLMs) still struggle with hallucinations despite their impressive capabilities. Recent studies have attempted to mitigate this by applying Direct…