Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations
Authors: Aditya Kalyanpur, Tom Breloff, David A Ferrucci10867-10874
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we evaluate Braid on the ROC Story Cloze test and achieve close to stateof-the-art results while providing frame-based explanations. |
| Researcher Affiliation | Industry | Aditya Kalyanpur, Tom Breloff, David A Ferrucci Elemental Cognition Inc. {adityak, tomb, davef} @ec.ai |
| Pseudocode | Yes | Detailed pseudo-code of the proof-graph builder algorithms are in the supplementary material (Appendix). |
| Open Source Code | No | The paper does not provide a direct link to the source code for Braid or explicitly state that it is being released. |
| Open Datasets | Yes | We evaluate Braid on the ROC Story Cloze test and achieve close to stateof-the-art results while providing frame-based explanations. |
| Dataset Splits | Yes | For our experiments, we focus on the Spring 2016 ROC dataset, which has a validation set and a test set of 1871 examples each. ... We used a 90/10 split for train/dev and found that the classifier accuracy to be 87.5% on the dev set. |
| Hardware Specification | No | All DRG models were trained on a single GPU with an effective batch size of 64, a learning rate of 1e-3 and warmup=100 steps. (This only mentions "a single GPU" without specifying the model or other hardware details). |
| Software Dependencies | No | The paper mentions software like "T5-base" and "BERT" but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | All DRG models were trained on a single GPU with an effective batch size of 64, a learning rate of 1e-3 and warmup=100 steps. Models for frame-detection (step 1) were trained for 100 epochs, while models for ending-prediction (step 2) were trained for 10 epochs. |