Interacting with Explanations through Critiquing
Authors: Diego Antognini, Claudiu Musat, Boi Faltings
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results show that our system achieves good performance in adapting to the preferences expressed in multi-step critiquing and generates consistent explanations. We evaluate our model using two real-world recommendation datasets. |
| Researcher Affiliation | Collaboration | 1 Ecole Polytechnique F ed erale de Lausanne, Switzerland 2Swisscom, Switzerland {diego.antognini, boi.faltings}@epfl.ch, claudiu.musat@swisscom.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing its source code or a direct link to a code repository for the described methodology. A link to appendices is provided, but it's not a code repository. |
| Open Datasets | Yes | We evaluate the quantitative performance of TRECS using two real-world, publicly available datasets: Beer Advocate [Mc Auley and Leskovec, 2013] and Hotel Rec [Antognini and Faltings, 2020]. |
| Dataset Splits | Yes | We keep the first 80% of interactions per user as the training data, leaving the remaining 20% for validation and testing. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions using "Adam" as an optimizer but does not provide specific version numbers for any software components, libraries, or programming languages used. |
| Experiment Setup | Yes | We set the embedding and attention dimension to 256 and to 1024 for the feed-forward network. The encoder and decoder consist of two layers of Transformer with 4 attention heads. We use a batch size of 128, dropout of 0.1, and Adam with learning rate 0.001. For critiquing, we choose a threshold and decay coefficient T = 0.015, ζ = 0.9 and T = 0.01, ζ = 0.975 for hotel and beer reviews. |