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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
Authors: Aaron Chan, Jiashu Xu, Boyuan Long, Soumya Sanyal, Tanishq Gupta, Xiang Ren
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 25,269 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,194 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On three commonsense QA benchmarks (CSQA, OBQA, CODAH) and a range of KG-augmented models, we show that SALKG can yield considerable performance gains up to 2.76% absolute improvement on CSQA. |
| Researcher Affiliation | Academia | University of Southern California, IIT Delhi EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm', nor are there any structured code-like blocks outlining procedures. |
| Open Source Code | Yes | Code and data are available at: https://github.com/INK-USC/Sal KG. |
| Open Datasets | Yes | We use the CSQA [52] and OBQA [39] multi-choice QA datasets. ... As in prior works, we use the Concept Net [49] KG for both datasets. |
| Dataset Splits | No | For CSQA, we use the accepted in-house data split from [31], as the official test labels are not public. |
| Hardware Specification | No | The paper does not specify the exact hardware components (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like BERT, RoBERTa, and PyTorch, but it does not specify their version numbers or other ancillary software dependencies with specific versioning for reproducibility. |
| Experiment Setup | Yes | We use thresholds T = 0.01 and k = 10 for coarse and fine explanations, respectively. For text encoders, we use BERT(-Base) [11] and Ro BERTa(-Large) [35]. For graph encoders, we use MHGRN [13], Path Gen [56], and Relation Network (RN) [46, 31]. ... LS = Ltask + Ξ»Lsal, where Ξ» 0 is a loss weighting parameter. |