5 papers
MaPPO: Maximum a Posteriori Preference Optimization with Prior Knowledge
Guangchen Lan, Sipeng Zhang, Tianle Wang +7
As the era of large language models (LLMs) unfolds, Preference Optimization (PO) methods have become a central approach to aligning LLMs with human preferences and improving perfor…
Object-Centric Data Synthesis for Category-level Object Detection
Vikhyat Agarwal, Jiayi Cora Guo, Declan Hoban +5
Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model's detection capability to new object…
LaMPE: Length-aware Multi-grained Positional Encoding for Adaptive Long-context Scaling Without Training
Sikui Zhang, Guangze Gao, Ziyun Gan +5
Large language models (LLMs) experience significant performance degradation when the input exceeds the pretraining context window, primarily due to the out-of-distribution (OOD) be…
Dual Decomposition of Weights and Singular Value Low Rank Adaptation
Jialong Han, Si Zhang, Ke Zhang
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a critical paradigm for adapting Large Language Models (LLMs) to downstream tasks, among which Low-rank Adaptation (LoRA) repr…
OSoRA: Output-Dimension and Singular-Value Initialized Low-Rank Adaptation
Jialong Han, Si Zhang, Ke Zhang
Fine-tuning Large Language Models (LLMs) has become increasingly challenging due to their massive scale and associated computational costs. Parameter-Efficient Fine-Tuning (PEFT) m…