Zobrazeno 1 - 10
of 1 348
pro vyhledávání: '"Xing Rui"'
The capabilities of Large Language Models (LLMs) have significantly evolved, extending from natural language processing to complex tasks like code understanding and generation. We expand the scope of LLMs' capabilities to a broader context, using LLM
Externí odkaz:
http://arxiv.org/abs/2410.06667
Autor:
Li, Haonan, Han, Xudong, Wang, Hao, Wang, Yuxia, Wang, Minghan, Xing, Rui, Geng, Yilin, Zhai, Zenan, Nakov, Preslav, Baldwin, Timothy
We introduce Loki, an open-source tool designed to address the growing problem of misinformation. Loki adopts a human-centered approach, striking a balance between the quality of fact-checking and the cost of human involvement. It decomposes the fact
Externí odkaz:
http://arxiv.org/abs/2410.01794
UAVs emerge as the optimal carriers for visual weed iden?tification and integrated pest and disease management in crops. How?ever, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we ha
Externí odkaz:
http://arxiv.org/abs/2409.15679
Autor:
Vazhentsev, Artem, Fadeeva, Ekaterina, Xing, Rui, Panchenko, Alexander, Nakov, Preslav, Baldwin, Timothy, Panov, Maxim, Shelmanov, Artem
Uncertainty quantification (UQ) is a perspective approach to detecting Large Language Model (LLM) hallucinations and low quality output. In this work, we address one of the challenges of UQ in generation tasks that arises from the conditional depende
Externí odkaz:
http://arxiv.org/abs/2408.10692
Autor:
Abassy, Mervat, Elozeiri, Kareem, Aziz, Alexander, Ta, Minh Ngoc, Tomar, Raj Vardhan, Adhikari, Bimarsha, Ahmed, Saad El Dine, Wang, Yuxia, Afzal, Osama Mohammed, Xie, Zhuohan, Mansurov, Jonibek, Artemova, Ekaterina, Mikhailov, Vladislav, Xing, Rui, Geng, Jiahui, Iqbal, Hasan, Mujahid, Zain Muhammad, Mahmoud, Tarek, Tsvigun, Akim, Aji, Alham Fikri, Shelmanov, Artem, Habash, Nizar, Gurevych, Iryna, Nakov, Preslav
The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, part
Externí odkaz:
http://arxiv.org/abs/2408.04284
Estimating cardinality, i.e., the number of distinct elements, of a data stream is a fundamental problem in areas like databases, computer networks, and information retrieval. This study delves into a broader scenario where each element carries a pos
Externí odkaz:
http://arxiv.org/abs/2406.19143
Autor:
Vashurin, Roman, Fadeeva, Ekaterina, Vazhentsev, Artem, Rvanova, Lyudmila, Tsvigun, Akim, Vasilev, Daniil, Xing, Rui, Sadallah, Abdelrahman Boda, Grishchenkov, Kirill, Petrakov, Sergey, Panchenko, Alexander, Baldwin, Timothy, Nakov, Preslav, Panov, Maxim, Shelmanov, Artem
Uncertainty quantification (UQ) is a critical component of machine learning (ML) applications. The rapid proliferation of large language models (LLMs) has stimulated researchers to seek efficient and effective approaches to UQ for text generation. As
Externí odkaz:
http://arxiv.org/abs/2406.15627
Automated fact-checking systems often struggle with trustworthiness, as their generated explanations can include hallucinations. In this work, we explore evidence attribution for fact-checking explanation generation. We introduce a novel evaluation p
Externí odkaz:
http://arxiv.org/abs/2406.12645
Publikováno v:
SHS Web of Conferences, Vol 157, p 02017 (2023)
With the rapid development of information technology, relying on the network platform to innovate the form of education is no longer unfamiliar. At the same time, the economic development makes the education conditions in rural areas continue to impr
Externí odkaz:
https://doaj.org/article/3a36cbb83cc74580a2db8743cf6566e9
Autor:
Xing, Rui1,2 (AUTHOR) xrui0802@163.com, Guo, Pengcheng1,3 (AUTHOR) gpch860429@163.com
Publikováno v:
Materials (1996-1944). Sep2024, Vol. 17 Issue 17, p4219. 15p.