Zobrazeno 1 - 10
of 424
pro vyhledávání: '"Chen, Hailin"'
Autor:
Nguyen, Xuan-Phi, Pandit, Shrey, Purushwalkam, Senthil, Xu, Austin, Chen, Hailin, Ming, Yifei, Ke, Zixuan, Savarese, Silvio, Xong, Caiming, Joty, Shafiq
Retrieval Augmented Generation (RAG), a paradigm that integrates external contextual information with large language models (LLMs) to enhance factual accuracy and relevance, has emerged as a pivotal area in generative AI. The LLMs used in RAG applica
Externí odkaz:
http://arxiv.org/abs/2409.09916
Autor:
Ravaut, Mathieu, Ding, Bosheng, Jiao, Fangkai, Chen, Hailin, Li, Xingxuan, Zhao, Ruochen, Qin, Chengwei, Xiong, Caiming, Joty, Shafiq
With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical. Business applications and fundraising in AI have reached a scale
Externí odkaz:
http://arxiv.org/abs/2404.00699
Autor:
Chen, Hailin, Jiao, Fangkai, Li, Xingxuan, Qin, Chengwei, Ravaut, Mathieu, Zhao, Ruochen, Xiong, Caiming, Joty, Shafiq
Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce. Through instruction-tuning a large language model (LLM) with supervised fine-tuning and reinforcement learning from human
Externí odkaz:
http://arxiv.org/abs/2311.16989
With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs. Previous distillation methods usually prompt ChatGPT to generate a set of
Externí odkaz:
http://arxiv.org/abs/2310.18628
Large Language Models (LLMs) have already become quite proficient at solving simpler programming tasks like those in HumanEval or MBPP benchmarks. However, solving more complex and competitive programming tasks is still quite challenging for these mo
Externí odkaz:
http://arxiv.org/abs/2310.08992
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown promising results in language understanding tasks, especially in low-re
Externí odkaz:
http://arxiv.org/abs/2308.03117
Autor:
Zhao, Ruochen, Chen, Hailin, Wang, Weishi, Jiao, Fangkai, Do, Xuan Long, Qin, Chengwei, Ding, Bosheng, Guo, Xiaobao, Li, Minzhi, Li, Xingxuan, Joty, Shafiq
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world. However, there lacks a unified perception of at whi
Externí odkaz:
http://arxiv.org/abs/2303.10868
Machine learning models usually assume i.i.d data during training and testing, but data and tasks in real world often change over time. To emulate the transient nature of real world, we propose a challenging but practical task: text classification in
Externí odkaz:
http://arxiv.org/abs/2211.17142
Publikováno v:
In China Economic Review October 2024 87
Publikováno v:
In Resources Policy September 2024 96