Can Transformers Smell Like Humans?
Authors: Farzaneh Taleb, Miguel Vasco, Antonio Ribeiro, Mårten Björkman, Danica Kragic
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use multiple datasets and different types of perceptual representations to show that the representations encoded by transformer models are able to predict: (i) labels associated with odorants provided by experts; (ii) continuous ratings provided by human participants with respect to pre-defined descriptors; and (iii) similarity ratings between odorants provided by human participants. Our experiments do not require significant computational resources: we mostly train linear models that do not involve GPU usage or models that can be trained on a single commercially available GPU under one hour. |
| Researcher Affiliation | Academia | Farzaneh Taleb Department of Intelligent Systems KTH Royal Institute of Technology Stockholm, Sweden farzantn@kth.se Miguel Vasco Department of Intelligent Systems KTH Royal Institute of Technology Stockholm, Sweden miguelsv@kth.se Antônio H. Ribeiro Department of Information Technology Uppsala University Uppsala, Sweden antonio.horta.ribeiro@it.uu.se Mårten Björkman Department of Intelligent Systems KTH Royal Institute of Technology Stockholm, Sweden celle@kth.se Danica Kragic Department of Intelligent Systems KTH Royal Institute of Technology Stockholm, Sweden dani@kth.se |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All computational code to reproduce our results is available at https://github.com/Farzaneh-Taleb/transformer-olfactory-alignment |
| Open Datasets | Yes | We use the publicly available version of the following datasets provided by Pyrfume repository [31]. Leffingwell-Goodscent (GS-LF) [27, 28]. ... Sagar [33]. ... Keller [34]. ... Ravia [17]. ... Snitz [18]. |
| Dataset Splits | Yes | We use nested 5-fold cross-validation to tune the hyper-parameters of the linear models and assess evaluation metrics on the test set that was held out during the training phase using an 80%-20% train-test split. |
| Hardware Specification | No | Our experiments do not require significant computational resources: we mostly train linear models that do not involve GPU usage or models that can be trained on a single commercially available GPU under one hour. |
| Software Dependencies | No | The paper mentions software and models used (e.g., Mo LFormer [25], Open-POM [32], Alva Desc [35]) but does not provide specific version numbers for these or for any other general software dependencies (e.g., programming languages, libraries). |
| Experiment Setup | Yes | We use nested 5-fold cross-validation to tune the hyper-parameters of the linear models and assess evaluation metrics on the test set that was held out during the training phase using an 80%-20% train-test split. This process is repeated 30 times using 30 different train-test splits. ... First, the dimensionality of the extracted representations is reduced to 20 using PCA, followed by z-scoring of each feature. Then, we train individual logistic regression models for each descriptor. |