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 [1].
A Knowledge Representation that Models Memory in Narrative Comprehension
Authors: Rogelio Cardona-Rivera, Robert Young
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Future work will validate the INDEXTER model using data collected during the construction of the EIM cognitive conceptual model, with the goal of using INDEXTER to generate narratives designed to achieve a particular mental (memory) configuration. |
| Researcher Affiliation | Academia | Rogelio E. Cardona-Rivera and R. Michael Young Campus Box 8206, Raleigh, NC, USA, 27695 Liquid Narrative Group North Carolina State University EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It provides conceptual definitions and an equation. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | Future work will validate the INDEXTER model using data collected during the construction of the EIM cognitive conceptual model... No dataset is used or made publicly available in this paper. |
| Dataset Splits | No | The paper does not describe experiments, nor does it provide any dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not describe any experiments, and therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper mentions theoretical frameworks like 'STRIPS-like' and 'POCL planning' but does not list any specific software dependencies with version numbers used for implementation. |
| Experiment Setup | No | The paper does not describe any experiments, and therefore no experimental setup details, hyperparameters, or training settings are provided. |