Zobrazeno 1 - 10
of 9 765
pro vyhledávání: '"An-Yu Shi"'
Autor:
Li, Xinze, Mei, Sen, Liu, Zhenghao, Yan, Yukun, Wang, Shuo, Yu, Shi, Zeng, Zheni, Chen, Hao, Yu, Ge, Liu, Zhiyuan, Sun, Maosong, Xiong, Chenyan
Retrieval-Augmented Generation (RAG) has proven its effectiveness in mitigating hallucinations in Large Language Models (LLMs) by retrieving knowledge from external resources. To adapt LLMs for RAG pipelines, current approaches use instruction tuning
Externí odkaz:
http://arxiv.org/abs/2410.13509
Autor:
Yu, Shi, Tang, Chaoyue, Xu, Bokai, Cui, Junbo, Ran, Junhao, Yan, Yukun, Liu, Zhenghao, Wang, Shuo, Han, Xu, Liu, Zhiyuan, Sun, Maosong
Retrieval-augmented generation (RAG) is an effective technique that enables large language models (LLMs) to utilize external knowledge sources for generation. However, current RAG systems are solely based on text, rendering it impossible to utilize v
Externí odkaz:
http://arxiv.org/abs/2410.10594
Autor:
Wang, Ruobing, Zha, Daren, Yu, Shi, Zhao, Qingfei, Chen, Yuxuan, Wang, Yixuan, Wang, Shuo, Yan, Yukun, Liu, Zhenghao, Han, Xu, Liu, Zhiyuan, Sun, Maosong
Retrieval-Augmented Generation (RAG) mitigates issues of the factual errors and hallucinated outputs generated by Large Language Models (LLMs) in open-domain question-answering tasks (OpenQA) via introducing external knowledge. For complex QA, howeve
Externí odkaz:
http://arxiv.org/abs/2410.08821
Autor:
Chen, Yingfa, Hu, Chenlong, Feng, Cong, Song, Chenyang, Yu, Shi, Han, Xu, Liu, Zhiyuan, Sun, Maosong
This study presents a multi-modal multi-granularity tokenizer specifically designed for analyzing ancient Chinese scripts, focusing on the Chu bamboo slip (CBS) script used during the Spring and Autumn and Warring States period (771-256 BCE) in Ancie
Externí odkaz:
http://arxiv.org/abs/2409.01011
Autor:
Zhu, Kunlun, Luo, Yifan, Xu, Dingling, Wang, Ruobing, Yu, Shi, Wang, Shuo, Yan, Yukun, Liu, Zhenghao, Han, Xu, Liu, Zhiyuan, Sun, Maosong
Retrieval-Augmented Generation (RAG) is a powerful approach that enables large language models (LLMs) to incorporate external knowledge. However, evaluating the effectiveness of RAG systems in specialized scenarios remains challenging due to the high
Externí odkaz:
http://arxiv.org/abs/2408.01262
Publikováno v:
Sci. China Phys. Mech. Astron. 67, 290411 (2024)
Recently, much attention has been focused on the false vacuum islands that are flooded by an expanding ocean of true-vacuum bubbles slightly later than most of the other parts of the world. These delayed decay regions will accumulate locally larger v
Externí odkaz:
http://arxiv.org/abs/2404.06506
Large language models (LLMs) require lengthy prompts as the input context to produce output aligned with user intentions, a process that incurs extra costs during inference. In this paper, we propose the Gist COnditioned deCOding (Gist-COCO) model, i
Externí odkaz:
http://arxiv.org/abs/2402.16058
Autor:
Xu, Zhipeng, Liu, Zhenghao, Yan, Yukun, Wang, Shuo, Yu, Shi, Zeng, Zheni, Xiao, Chaojun, Liu, Zhiyuan, Yu, Ge, Xiong, Chenyan
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to leverage external knowledge, enhancing their performance on knowledge-intensive tasks. However, existing RAG models often treat LLMs as passive recipients of information, wh
Externí odkaz:
http://arxiv.org/abs/2402.13547
Autor:
Li, Yunnan, Yu, Shi
Any finite-dimensional commutative (associative) graded algebra with all nonzero homogeneous subspaces one-dimensional is defined by a symmetric coefficient matrix. This algebraic structure gives a basic kind of $A$-graded algebras originally studied
Externí odkaz:
http://arxiv.org/abs/2311.16403
Publikováno v:
Phys. Rev. D 109 (2024) 9, 096012
The strongly coupled system like the quark-hadron transition (if it is of first order) is becoming an active play yard for the physics of cosmological first-order phase transitions. However, the traditional field theoretic approach to strongly couple
Externí odkaz:
http://arxiv.org/abs/2311.07347