no code implementations • 3 Feb 2025 • Huaye Zeng, Dongfu Jiang, Haozhe Wang, Ping Nie, Xiaotong Chen, Wenhu Chen
Notably, we follow the R1-style training to start from Qwen2. 5-Coder-base directly and show that our RL training can improve model on HumanEval-plus by over 25\% and MBPP-plus by 6\% for merely 80 optimization steps.
no code implementations • 10 Dec 2024 • Yao Fu, Yinsicheng Jiang, Yeqi Huang, Ping Nie, Zhan Lu, Leyang Xue, Congjie He, Man-Kit Sit, Jilong Xue, Li Dong, Ziming Miao, Kai Zou, Edoardo Ponti, Luo Mai
The sparse Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs) efficiently; however, MoE systems rely on heterogeneous compute and memory resources.
no code implementations • 20 Oct 2024 • Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
In this paper, we aim to investigate these internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and demonstrate how to improve RAG by examining expert activations in these LLMs.
no code implementations • 8 Apr 2024 • Giwon Hong, Aryo Pradipta Gema, Rohit Saxena, Xiaotang Du, Ping Nie, Yu Zhao, Laura Perez-Beltrachini, Max Ryabinin, Xuanli He, Clémentine Fourrier, Pasquale Minervini
Large Language Models (LLMs) have transformed the Natural Language Processing (NLP) landscape with their remarkable ability to understand and generate human-like text.
1 code implementation • 29 Oct 2022 • Yu Fei, Ping Nie, Zhao Meng, Roger Wattenhofer, Mrinmaya Sachan
We further explore the applicability of our clustering approach by evaluating it on 14 datasets with more diverse topics, text lengths, and numbers of classes.
no code implementations • 10 Sep 2022 • Yujie Lu, Huiliang Zhang, Ping Nie, Weixi Feng, Wenda Xu, Xin Eric Wang, William Yang Wang
In this paper, we propose an Unseen Discrepancy Anticipating Vision and Language Navigation (DAVIS) that learns to generalize to unseen environments via encouraging test-time visual consistency.
no code implementations • 22 Oct 2021 • Yujie Lu, Ping Nie, Shengyu Zhang, Ming Zhao, Ruobing Xie, William Yang Wang, Yi Ren
However, existing work are primarily built upon pre-defined retrieval channels, including User-CF (U2U), Item-CF (I2I), and Embedding-based Retrieval (U2I), thus access to the limited correlation between users and items which solely entail from partial information of latent interactions.
no code implementations • 16 Sep 2020 • Ping Nie, Yuyu Zhang, Arun Ramamurthy, Le Song
Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered.
Ranked #16 on
Question Answering
on HotpotQA
no code implementations • 2 Jun 2020 • Jie Cai, Zhengzhou Zhu, Ping Nie, Qian Liu
In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e. g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task.
no code implementations • 28 Feb 2020 • Yuyu Zhang, Ping Nie, Xiubo Geng, Arun Ramamurthy, Le Song, Daxin Jiang
Recent studies on open-domain question answering have achieved prominent performance improvement using pre-trained language models such as BERT.
2 code implementations • NeurIPS 2019 • Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on
Chinese Sentence Pair Classification
on LCQMC
Chinese Dependency Parsing
Chinese Named Entity Recognition
+21