Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Cao, Ziqiang"'
Autor:
Xie, Yu, Qiao, Qian, Gao, Jun, Wu, Tianxiang, Huang, Shaoyao, Fan, Jiaqing, Cao, Ziqiang, Wang, Zili, Zhang, Yue, Zhang, Jielei, Sun, Huyang
More and more end-to-end text spotting methods based on Transformer architecture have demonstrated superior performance. These methods utilize a bipartite graph matching algorithm to perform one-to-one optimal matching between predicted objects and a
Externí odkaz:
http://arxiv.org/abs/2408.00355
In-context learning (ICL) facilitates Large Language Models (LLMs) exhibiting emergent ability on downstream tasks without updating billions of parameters. However, in the area of multi-modal Large Language Models (MLLMs), two problems hinder the app
Externí odkaz:
http://arxiv.org/abs/2406.07588
ChatGPT is instruct-tuned to generate general and human-expected content to align with human preference through Reinforcement Learning from Human Feedback (RLHF), meanwhile resulting in generated responses not salient enough. Therefore, in this case,
Externí odkaz:
http://arxiv.org/abs/2406.01070
Large language models (LLMs) have demonstrated the capacity to improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. Two strategies are designed to perform this iterative proce
Externí odkaz:
http://arxiv.org/abs/2406.00507
In-context learning (ICL) facilitates large language models (LLMs) exhibiting spectacular emergent capabilities in various scenarios. Unfortunately, introducing demonstrations easily makes the prompt length explode, bringing a significant burden to h
Externí odkaz:
http://arxiv.org/abs/2405.17062
Long prompt leads to huge hardware costs when using transformer-based Large Language Models (LLMs). Unfortunately, many tasks, such as summarization, inevitably introduce long documents, and the wide application of in-context learning easily makes th
Externí odkaz:
http://arxiv.org/abs/2405.17052
Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annotated data, data augmentation is commonly used for training coherence ev
Externí odkaz:
http://arxiv.org/abs/2404.00681
With the rapid development of large language models, AI assistants like ChatGPT have widely entered people's works and lives. In this paper, we present an evolving large language model assistant that utilizes verbal long-term memory. It focuses on pr
Externí odkaz:
http://arxiv.org/abs/2312.17257
Autor:
Geng, Lei, Yan, Xu, Cao, Ziqiang, Li, Juntao, Li, Wenjie, Li, Sujian, Zhou, Xinjie, Yang, Yang, Zhang, Jun
Most biomedical pretrained language models are monolingual and cannot handle the growing cross-lingual requirements. The scarcity of non-English domain corpora, not to mention parallel data, poses a significant hurdle in training multilingual biomedi
Externí odkaz:
http://arxiv.org/abs/2311.11564
Publikováno v:
NLPCC 2023
Chinese Spelling Check (CSC) refers to the detection and correction of spelling errors in Chinese texts. In practical application scenarios, it is important to make CSC models have the ability to correct errors across different domains. In this paper
Externí odkaz:
http://arxiv.org/abs/2308.08176