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].
Leveraging Argumentation for Generating Robust Sample-based Explanations
Authors: Leila Amgoud, Philippe Muller, Henri Trenquier
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The fourth contribution is an experimental analysis of the functions on various datasets. The results conο¬rm that abductive explanations that are generated from datasets (as done by Anchors) are generally incorrect. They show also that the new functions which guarantee correctness perform well as they explain quite an important proportion of instances. |
| Researcher Affiliation | Academia | Leila Amgoud1 , Philippe Muller2 and Henri Trenquier3 1CNRS IRIT 2Toulouse University IRIT 3Toulouse University ANITI EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | Details on the implementation are given in the supplementary material. |
| Open Datasets | Yes | We tested the four functions on various datasets, namely diabetes, titanic available on the Kaggle website, and lending adult (shortened) and recidivism (shortened) that are available on Anchors experiments [Ribeiro et al., 2018]. |
| Dataset Splits | No | The paper mentions using 'training instances' and 'dataset on which R is trained' for the general context of classifiers, and discusses generating arguments from 'different percentages of the whole space', but it does not specify the train/validation/test splits for the datasets used in its own experiments. |
| Hardware Specification | No | The paper does not provide any specific hardware specifications (e.g., CPU, GPU models, or memory details) used for running its experiments. |
| Software Dependencies | No | The paper mentions that five functions were implemented, but it does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values or training configurations in the main text. It only vaguely states that 'Details on the implementation are given in the supplementary material.' |