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].
Explainable Certain Answers
Authors: Giovanni Amendola, Leonid Libkin
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a general framework for reasoning about them, and show that for open and closed world relational databases, they are precisely the common intersection-based notions of certainty. and Our main contribution is to extend the theory of informativeness-based notion of certainty to include explanations of why tuples appear in the answer, and to relate explainable certain answers to the classical intersection-based approach. |
| Researcher Affiliation | Academia | Giovanni Amendola1, Leonid Libkin2 1 Mathematics and Computer Science, University of Calabria 2 School of Informatics, University of Edinburgh |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for the described methodology. |
| Open Datasets | No | The paper does not describe any experimental setup involving datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, so no dataset split information (e.g., train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or computations that would require specifying hardware details. |
| Software Dependencies | No | The paper is theoretical and does not describe any implementation details that would require specifying software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |