Zobrazeno 1 - 10
of 2 880
pro vyhledávání: '"WANG Yulong"'
Fine-tuning large language models (LLMs) has become essential for adapting pretrained models to specific downstream tasks. In this paper, we propose Linear Chain Transformation (LinChain), a novel approach that introduces a sequence of linear transfo
Externí odkaz:
http://arxiv.org/abs/2411.00039
In this paper, we introduce the big.LITTLE Vision Transformer, an innovative architecture aimed at achieving efficient visual recognition. This dual-transformer system is composed of two distinct blocks: the big performance block, characterized by it
Externí odkaz:
http://arxiv.org/abs/2410.10267
Autor:
Sasaki, Yuya, Wang, Yulong
We introduce a novel method for estimating and conducting inference about extreme quantile treatment effects (QTEs) in the presence of endogeneity. Our approach is applicable to a broad range of empirical research designs, including instrumental vari
Externí odkaz:
http://arxiv.org/abs/2409.03979
Existing agents based on large language models (LLMs) demonstrate robust problem-solving capabilities by integrating LLMs' inherent knowledge, strong in-context learning and zero-shot capabilities, and the use of tools combined with intricately desig
Externí odkaz:
http://arxiv.org/abs/2407.10718
Autor:
Yu, Yingpeng, Liu, Zhaolong, Li, Qi, Chen, Zhaoxu, Wang, Yulong, Hao, Munan, Yang, Yaling, Gong, Chunsheng, Chen, Long, Xie, Zhenkai, Zhou, Kaiyao, Ren, Huifen, Chen, Xu, Jin, Shifeng
We present a comprehensive investigation of the superconducting properties of ZrRe2, a Re-based hexagonal Laves compounds. ZrRe2 crystallizes in a C14-type structure (space group P63/mmc), with cell parameters a=b=5.2682(5) and c=8.63045 . Resistivit
Externí odkaz:
http://arxiv.org/abs/2407.10268
Autor:
Ju, Tianjie, Wang, Yiting, Ma, Xinbei, Cheng, Pengzhou, Zhao, Haodong, Wang, Yulong, Liu, Lifeng, Xie, Jian, Zhang, Zhuosheng, Liu, Gongshen
The rapid adoption of large language models (LLMs) in multi-agent systems has highlighted their impressive capabilities in various applications, such as collaborative problem-solving and autonomous negotiation. However, the security implications of t
Externí odkaz:
http://arxiv.org/abs/2407.07791
Autor:
Lv, Ang, Chen, Yuhan, Zhang, Kaiyi, Wang, Yulong, Liu, Lifeng, Wen, Ji-Rong, Xie, Jian, Yan, Rui
In this paper, we delve into several mechanisms employed by Transformer-based language models (LLMs) for factual recall tasks. We outline a pipeline consisting of three major steps: (1) Given a prompt ``The capital of France is,'' task-specific atten
Externí odkaz:
http://arxiv.org/abs/2403.19521
Neural implicit representation of geometric shapes has witnessed considerable advancements in recent years. However, common distance field based implicit representations, specifically signed distance field (SDF) for watertight shapes or unsigned dist
Externí odkaz:
http://arxiv.org/abs/2403.01414
Motivated by the empirical observation of power-law distributions in the credits (e.g., "likes") of viral social media posts, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its paramete
Externí odkaz:
http://arxiv.org/abs/2403.01318
Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation
Externí odkaz:
http://arxiv.org/abs/2402.05827