Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Zheng, Guoqing"'
Autor:
Mitra, Arindam, Del Corro, Luciano, Zheng, Guoqing, Mahajan, Shweti, Rouhana, Dany, Codas, Andres, Lu, Yadong, Chen, Wei-ge, Vrousgos, Olga, Rosset, Corby, Silva, Fillipe, Khanpour, Hamed, Lara, Yash, Awadallah, Ahmed
Synthetic data is becoming increasingly important for accelerating the development of language models, both large and small. Despite several successful use cases, researchers also raised concerns around model collapse and drawbacks of imitating other
Externí odkaz:
http://arxiv.org/abs/2407.03502
The remarkable abilities of large language models (LLMs) like GPT-4 partially stem from post-training processes like Reinforcement Learning from Human Feedback (RLHF) involving human preferences encoded in a reward model. However, these reward models
Externí odkaz:
http://arxiv.org/abs/2312.02206
Autor:
Mitra, Arindam, Del Corro, Luciano, Mahajan, Shweti, Codas, Andres, Simoes, Clarisse, Agarwal, Sahaj, Chen, Xuxi, Razdaibiedina, Anastasia, Jones, Erik, Aggarwal, Kriti, Palangi, Hamid, Zheng, Guoqing, Rosset, Corby, Khanpour, Hamed, Awadallah, Ahmed
Orca 1 learns from rich signals, such as explanation traces, allowing it to outperform conventional instruction-tuned models on benchmarks like BigBench Hard and AGIEval. In Orca 2, we continue exploring how improved training signals can enhance smal
Externí odkaz:
http://arxiv.org/abs/2311.11045
Autor:
Dun, Chen, Garcia, Mirian Hipolito, Zheng, Guoqing, Awadallah, Ahmed Hassan, Kyrillidis, Anastasios, Sim, Robert
Large Language Models (LLMs) have the ability to solve a variety of tasks, such as text summarization and mathematical questions, just out of the box, but they are often trained with a single task in mind. Due to high computational costs, the current
Externí odkaz:
http://arxiv.org/abs/2310.02842
Autor:
Xia, Menglin, Zhang, Xuchao, Couturier, Camille, Zheng, Guoqing, Rajmohan, Saravan, Ruhle, Victor
Large language models (LLMs) enhanced with retrieval augmentation has shown great performance in many applications. However, the computational demands for these models pose a challenge when applying them to real-time tasks, such as composition assist
Externí odkaz:
http://arxiv.org/abs/2308.04215
Publikováno v:
Shipin Kexue, Vol 45, Iss 15, Pp 139-147 (2024)
In order to investigate the nutritional value and small molecule metabolite composition of Taihe silky chicken eggs, this study comparatively analyzed the appearance, egg quality and hydrolyzed amino acid content of Taihe silky chicken eggs and Mahua
Externí odkaz:
https://doaj.org/article/52e364e5dd3c406990a01cb16d9e5e7c
Publikováno v:
Zhongguo youzhi, Vol 49, Iss 8, Pp 103-110 (2024)
旨在为芝麻产业发展提供文献支撑,利用CiteSpace及Excel软件,基于2012—2022年Web of Science(WoS)核心合集数据库和中国知网(CNKI)学术期刊数据库中的中文核心期刊,采用文献计量方法对芝麻研
Externí odkaz:
https://doaj.org/article/9decc49f8fd9445f99f26878db927d7b
Autor:
Dun, Chen, Garcia, Mirian Hipolito, Zheng, Guoqing, Awadallah, Ahmed Hassan, Sim, Robert, Kyrillidis, Anastasios, Dimitriadis, Dimitrios
One of the goals in Federated Learning (FL) is to create personalized models that can adapt to the context of each participating client, while utilizing knowledge from a shared global model. Yet, often, personalization requires a fine-tuning step usi
Externí odkaz:
http://arxiv.org/abs/2306.08586
Recent work has shown that language models (LMs) trained with multi-task \textit{instructional learning} (MTIL) can solve diverse NLP tasks in zero- and few-shot settings with improved performance compared to prompt tuning. MTIL illustrates that LMs
Externí odkaz:
http://arxiv.org/abs/2210.11617
The success of graph neural networks on graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only few labeled nodes are available, how to improve their robustness is a key to achieve re
Externí odkaz:
http://arxiv.org/abs/2208.12422