5 citations · 11 across the 17 of their papers we have counts for
13 papers · 1 filter
TaP: A Taxonomy-Guided Framework for Automated and Scalable Preference Data Generation
Renren Jin, Tianhao Shen, Xinwei Wu +9
Conducting supervised and preference fine-tuning of large language models (LLMs) requires high-quality datasets to improve their ability to follow instructions and align with human…
TailorKV: A Hybrid Framework for Long-Context Inference via Tailored KV Cache Optimization
Dingyu Yao, Bowen Shen, Zheng Lin +4
The Key-Value (KV) cache in generative large language models (LLMs) introduces substantial memory overhead. Existing works mitigate this burden by offloading or compressing the KV…
MobileIPL: Enhancing Mobile Agents Thinking Process via Iterative Preference Learning
Kun Huang, Weikai Xu, Yuxuan Liu +6
The Chain of Action-Planning Thoughts (CoaT) paradigm has been shown to improve the reasoning performance of VLM-based mobile agents in GUI tasks. However, the scarcity of diverse…
Mobile-Bench-v2: A More Realistic and Comprehensive Benchmark for VLM-based Mobile Agents
Weikai Xu, Zhizheng Jiang, Yuxuan Liu +7
VLM-based mobile agents are increasingly popular due to their capabilities to interact with smartphone GUIs and XML-structured texts and to complete daily tasks. However, existing…
ReachAgent: Enhancing Mobile Agent via Page Reaching and Operation
Qinzhuo Wu, Wei Liu, Jian Luan +1
Recently, mobile AI agents have gained increasing attention. Given a task, mobile AI agents can interact with mobile devices in multiple steps and finally form a GUI flow that solv…
DoTA: Weight-Decomposed Tensor Adaptation for Large Language Models
Xiaolin Hu, Xiang Cheng, Peiyu Liu +4
Low-rank adaptation (LoRA) reduces the computational and memory demands of fine-tuning large language models (LLMs) by approximating updates with low-rank matrices. However, low-ra…
MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding
Qinzhuo Wu, Weikai Xu, Wei Liu +6
Recently, mobile AI agents based on VLMs have been gaining increasing attention. These works typically utilize VLM as a foundation, fine-tuning it with instruction-based mobile dat…
HoPE: A Novel Positional Encoding Without Long-Term Decay for Enhanced Context Awareness and Extrapolation
Yuhan Chen, Ang Lv, Jian Luan +2
Many positional encodings (PEs) are designed to exhibit long-term decay, based on an entrenched and long-standing inductive opinion: tokens farther away from the current position c…
PMSS: Pretrained Matrices Skeleton Selection for LLM Fine-tuning
Qibin Wang, Xiaolin Hu, Weikai Xu +3
Low-rank adaptation (LoRA) and its variants have recently gained much interest due to their ability to avoid excessive inference costs. However, LoRA still encounters the following…
ToolPlanner: A Tool Augmented LLM for Multi Granularity Instructions with Path Planning and Feedback
Qinzhuo Wu, Wei Liu, Jian Luan +1
Recently, tool-augmented LLMs have gained increasing attention. Given an instruction, tool-augmented LLMs can interact with various external tools in multiple rounds and provide a…
Pruning Large Language Models to Intra-module Low-rank Architecture with Transitional Activations
Bowen Shen, Zheng Lin, Daren Zha +4
Structured pruning fundamentally reduces computational and memory overheads of large language models (LLMs) and offers a feasible solution for end-side LLM deployment. Structurally…
SUBLLM: A Novel Efficient Architecture with Token Sequence Subsampling for LLM
Quandong Wang, Yuxuan Yuan, Xiaoyu Yang +6
While Large Language Models (LLMs) have achieved remarkable success in various fields, the efficiency of training and inference remains a major challenge. To address this issue, we…
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
Renren Jin, Jiangcun Du, Wuwei Huang +4
Increasing the number of parameters in large language models (LLMs) usually improves performance in downstream tasks but raises compute and memory costs, making deployment difficul…