no code implementations • 4 Jan 2024 • Chen Zheng, Ke Sun, Da Tang, Yukun Ma, Yuyu Zhang, Chenguang Xi, Xun Zhou
The emergence of Large Language Models (LLMs) such as ChatGPT and LLaMA encounter limitations in domain-specific tasks, with these models often lacking depth and accuracy in specialized areas, and exhibiting a decrease in general capabilities when fine-tuned, particularly analysis ability in small sized models.
1 code implementation • 28 Sep 2023 • Shen Zheng, Yuyu Zhang, Yijie Zhu, Chenguang Xi, Pengyang Gao, Xun Zhou, Kevin Chen-Chuan Chang
With the rapid advancement of large language models (LLMs), there is a pressing need for a comprehensive evaluation suite to assess their capabilities and limitations.
no code implementations • ICLR 2022 • Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, Tao Qin
To open the black box of GNN and investigate these problems, we dissect state-of-the-art GNN modules for QA and analyze their reasoning capability.
Ranked #12 on Question Answering on OpenBookQA
no code implementations • 25 Apr 2021 • Yuyu Zhang, Heng Chi, Binghong Chen, Tsz Ling Elaine Tang, Lucia Mirabella, Le Song, Glaucio H. Paulino
We successfully apply our ONSG framework to computational morphogenesis, a representative and challenging class of PDE-constrained optimization problems.
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 • EMNLP 2020 • Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.
Ranked #1 on Question Answering on DROP Test
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.
1 code implementation • ICLR 2020 • Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
no code implementations • ACL 2019 • Yuyu Zhang, Le Song
Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language.
no code implementations • 5 Jun 2019 • Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.
no code implementations • 31 May 2018 • Yuyu Zhang, Hanjun Dai, Kamil Toraman, Le Song
Our model learns to reason with neural embeddings of both knowledge graphs.
1 code implementation • 12 Sep 2017 • Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song
Knowledge graph (KG) is known to be helpful for the task of question answering (QA), since it provides well-structured relational information between entities, and allows one to further infer indirect facts.
8 code implementations • NeurIPS 2017 • Hanjun Dai, Elias B. Khalil, Yuyu Zhang, Bistra Dilkina, Le Song
The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error.
1 code implementation • 20 Feb 2016 • Yuyu Zhang, Mohammad Taha Bahadori, Hang Su, Jimeng Sun
To achieve the best performance, it is often critical to select optimal algorithms and to set appropriate hyperparameters, which requires large computational efforts.
no code implementations • 27 Aug 2014 • Yuyu Zhang, Liang Pang, Lei Shi, Bin Wang
This paper describes the solution of Bazinga Team for Tmall Recommendation Prize 2014.
no code implementations • 23 Apr 2014 • Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, Tie-Yan Liu
Click prediction is one of the fundamental problems in sponsored search.
no code implementations • 29 Nov 2013 • Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.