Taking Up the Gaokao Challenge: An Information Retrieval Approach
Authors: Gong Cheng, Weixi Zhu, Ziwei Wang, Jianghui Chen, Yuzhong Qu
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach achieves encouraging results on real-life questions in recent history tests, significantly outperforming baseline approaches. |
| Researcher Affiliation | Academia | National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China |
| Pseudocode | Yes | Algorithm 1: Retrieving Concept Pages |
| Open Source Code | No | The paper states: 'We have made our dataset online accessible to the research community3.' with a link to the dataset, but no explicit statement or link for the source code of the methodology itself. |
| Open Datasets | Yes | We have made our dataset online accessible to the research community3. 3http://ws.nju.edu.cn/gaokao/ijcai-16/GaokaoHistory577.xml |
| Dataset Splits | No | The paper describes its dataset split into QS-A (123 questions) and QS-B (454 questions) used for evaluation. However, it does not provide explicit training, validation, or test dataset splits in terms of percentages, sample counts, or predefined partition files. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or specific solvers). |
| Experiment Setup | Yes | The parameters α = 0.8, β = 0.5, γ = 1.0 are empirically set in our experiments (Equation 1). The approach was configured to consistently use k = 6 top-ranked pages for answering every question. |