Zobrazeno 1 - 10
of 434
pro vyhledávání: '"LIU Shengping"'
Autor:
Liao, Huanxuan, He, Shizhu, Xu, Yao, Zhang, Yuanzhe, Hao, Yanchao, Liu, Shengping, Liu, Kang, Zhao, Jun
Large language models (LLMs) have acquired the ability to solve general tasks by utilizing instruction finetuning (IFT). However, IFT still relies heavily on instance training of extensive task data, which greatly limits the adaptability of LLMs to r
Externí odkaz:
http://arxiv.org/abs/2406.12382
Autor:
Ouyang, Jiayi, Liu, Shengping, Yang, Ziyue, Wang, Wei, Feng, Xue, Li, Yongzhuo, Huang, Yidong
In this article, we proposed a programmable 16-channel photonic solver for quadratic unconstrained binary optimization (QUBO) problems. The solver is based on a hybrid optoelectronic scheme including a photonic chip and the corresponding electronic d
Externí odkaz:
http://arxiv.org/abs/2407.04713
Retrieval-Augmented-Generation and Generation-Augmented-Generation have been proposed to enhance the knowledge required for question answering with Large Language Models (LLMs) by leveraging richer context. However, the former relies on external reso
Externí odkaz:
http://arxiv.org/abs/2403.15268
Large language models (LLMs) store extensive factual knowledge, but the underlying mechanisms remain unclear. Previous research suggests that factual knowledge is stored within multi-layer perceptron weights, and some storage units exhibit degeneracy
Externí odkaz:
http://arxiv.org/abs/2402.13731
Large language models internalize enormous parametric knowledge during pre-training. Concurrently, realistic applications necessitate external contextual knowledge to aid models on the underlying tasks. This raises a crucial dilemma known as knowledg
Externí odkaz:
http://arxiv.org/abs/2402.11893
As language models continue to scale in size and capability, they display an array of emerging behaviors, both beneficial and concerning. This heightens the need to control model behaviors. We hope to be able to control the personality traits of lang
Externí odkaz:
http://arxiv.org/abs/2402.10151
Data is one of the most critical elements in building a large language model. However, existing systems either fail to customize a corpus curation pipeline or neglect to leverage comprehensive corpus assessment for iterative optimization of the curat
Externí odkaz:
http://arxiv.org/abs/2311.12537
Autor:
Zhang, Baoli, Xie, Haining, Du, Pengfan, Chen, Junhao, Cao, Pengfei, Chen, Yubo, Liu, Shengping, Liu, Kang, Zhao, Jun
The unprecedented performance of large language models (LLMs) requires comprehensive and accurate evaluation. We argue that for LLMs evaluation, benchmarks need to be comprehensive and systematic. To this end, we propose the ZhuJiu benchmark, which h
Externí odkaz:
http://arxiv.org/abs/2308.14353
With the burgeoning development in the realm of large language models (LLMs), the demand for efficient incremental training tailored to specific industries and domains continues to increase. Currently, the predominantly employed frameworks lack modul
Externí odkaz:
http://arxiv.org/abs/2308.10252
Autor:
Weng, Yixuan, Zhu, Minjun, Xia, Fei, Li, Bin, He, Shizhu, Liu, Shengping, Sun, Bin, Liu, Kang, Zhao, Jun
Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks such as arithmetic, commonsense, and logical reasoning. However, LLMs wit
Externí odkaz:
http://arxiv.org/abs/2212.09561