Search Results for author: Jianhao Shen

Found 14 papers, 6 papers with code

Measuring Social Norms of Large Language Models

no code implementations3 Apr 2024 Ye Yuan, Kexin Tang, Jianhao Shen, Ming Zhang, Chenguang Wang

This enables the direct comparison of the social understanding of large language models to humans, more specifically, elementary students.

Measuring Vision-Language STEM Skills of Neural Models

no code implementations27 Feb 2024 Jianhao Shen, Ye Yuan, Srbuhi Mirzoyan, Ming Zhang, Chenguang Wang

Compared to existing datasets that often focus on examining expert-level ability, our dataset includes fundamental skills and questions designed based on the K-12 curriculum.


Lyra: Orchestrating Dual Correction in Automated Theorem Proving

1 code implementation27 Sep 2023 Chuanyang Zheng, Haiming Wang, Enze Xie, Zhengying Liu, Jiankai Sun, Huajian Xin, Jianhao Shen, Zhenguo Li, Yu Li

In addition, we introduce Conjecture Correction, an error feedback mechanism designed to interact with prover to refine formal proof conjectures with prover error messages.

 Ranked #1 on Automated Theorem Proving on miniF2F-test (Pass@100 metric)

Automated Theorem Proving Hallucination

FIMO: A Challenge Formal Dataset for Automated Theorem Proving

1 code implementation8 Sep 2023 Chengwu Liu, Jianhao Shen, Huajian Xin, Zhengying Liu, Ye Yuan, Haiming Wang, Wei Ju, Chuanyang Zheng, Yichun Yin, Lin Li, Ming Zhang, Qun Liu

We present FIMO, an innovative dataset comprising formal mathematical problem statements sourced from the International Mathematical Olympiad (IMO) Shortlisted Problems.

Automated Theorem Proving

A Comprehensive Survey on Deep Graph Representation Learning

no code implementations11 Apr 2023 Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, Junwei Yang, Jingyang Yuan, Yusheng Zhao, Yifan Wang, Xiao Luo, Ming Zhang

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.

Graph Embedding Graph Representation Learning

ComSearch: Equation Searching with Combinatorial Strategy for Solving Math Word Problems with Weak Supervision

no code implementations13 Oct 2022 Qianying Liu, Wenyu Guan, Jianhao Shen, Fei Cheng, Sadao Kurohashi

To address this problem, we propose a novel search algorithm with combinatorial strategy \textbf{ComSearch}, which can compress the search space by excluding mathematically equivalent equations.


Joint Language Semantic and Structure Embedding for Knowledge Graph Completion

1 code implementation COLING 2022 Jianhao Shen, Chenguang Wang, Linyuan Gong, Dawn Song

Unlike previous approaches that rely on either the structures or semantics of the knowledge graphs, we propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information.

Link Prediction

KGNN: Harnessing Kernel-based Networks for Semi-supervised Graph Classification

no code implementations21 May 2022 Wei Ju, Junwei Yang, Meng Qu, Weiping Song, Jianhao Shen, Ming Zhang

This problem is typically solved by using graph neural networks (GNNs), which yet rely on a large number of labeled graphs for training and are unable to leverage unlabeled graphs.

Graph Classification

Learning to Answer Ambiguous Questions with Knowledge Graph

no code implementations25 Dec 2019 Yikai Zhu, Jianhao Shen, Ming Zhang

In the task of factoid question answering over knowledge base, many questions have more than one plausible interpretation.

Question Answering

Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction

1 code implementation13 Mar 2018 Lu-chen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang

One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records.

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