24 citations · 51 across the 27 of their papers we have counts for
8 papers · 1 filter
ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
Yi Yang, Ziyu Lin, Liesheng Wei
Large-scale deep learning models impose substantial communication overh ead in distributed training, particularly in bandwidth-constrained or heterogeneous clo ud-edge environments…
SR-GRPO: Stable Rank as an Intrinsic Geometric Reward for Large Language Model Alignment
Yixuan Tang, Yi Yang
Aligning Large Language Models (LLMs) with human preferences typically relies on external supervision, which faces critical limitations: human annotations are scarce and subjective…
GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings
Yixuan Tang, Yi Yang
Domain-specific embedding models have shown promise for applications that require specialized semantic understanding, such as coding agents and financial retrieval systems, often a…
PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
Yixuan Tang, Yi Yang, Ahmed Abbasi
Recent advancements in Large Language Models (LLMs) demonstrate remarkable capabilities across various fields. These developments have led to more direct communication between huma…
Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models
Ningyuan Deng, Hanyu Duan, Yixuan Tang +1
Text embedding models are widely used in natural language processing applications. However, their capability is often benchmarked on tasks that do not require understanding nuanced…
Achieving binary weight and activation for LLMs using Post-Training Quantization
Siqing Song, Chuang Wang, Ruiqi Wang +2
Quantizing large language models (LLMs) to 1-bit precision significantly reduces computational costs, but existing quantization techniques suffer from noticeable performance degrad…
Adversarial Mixup Unlearning
Zhuoyi Peng, Yixuan Tang, Yi Yang
Machine unlearning is a critical area of research aimed at safeguarding data privacy by enabling the removal of sensitive information from machine learning models. One unique chall…
FinMTEB: Finance Massive Text Embedding Benchmark
Yixuan Tang, Yi Yang
Embedding models play a crucial role in representing and retrieving information across various NLP applications. Recent advances in large language models (LLMs) have further enhanc…