Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Enhancing Text Generation via Multi-Level Knowledge Aware Reasoning
Authors: Feiteng Mu, Wenjie Li
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method on two widely used datasets, experimental results demonstrate the effectiveness of our framework to text generation. |
| Researcher Affiliation | Academia | Feiteng Mu , Wenjie Li The Department of Computing, The Hong Kong Polytechnic University, Hong Kong EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | The stories come from ROCStories [Mostafazadeh et al., 2016] corpus. Following [Yao et al., 2019], we randomly split the dataset into 8:1:1 for training, validating and testing. Abductive NLG (αNLG) is to generate an explanatory hypothesis given two observations: O1 as the cause and O2 as the consequence. We use the official data split. |
| Dataset Splits | Yes | Following [Yao et al., 2019], we randomly split the dataset into 8:1:1 for training, validating and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using BART model and Adam optimizer but does not specify software names with version numbers for reproducibility. |
| Experiment Setup | Yes | To train the model, we use the Adam optimizer with β1 = 0.9, β2 = 0.999, ϵ = 10 6 and linearly decrease learning rate to zero with no warmup. We search for the best hyper-parameters according to BLEU-2 on the development set of each dataset. At the inference stage, we adopt beam search decoding with a beam size of 3 for our model and all the baselines we produce. |