Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Embedding Inference for Structured Multilabel Prediction
Authors: Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans
NeurIPS 2015 | Venue PDF | LLM Run Details | Input Tokens: 15,808 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,620 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that the benefits of structured output training can still be realized even after inference has been eliminated. |
| Researcher Affiliation | Collaboration | Farzaneh Mirzazadeh Siamak Ravanbakhsh University of Alberta EMAIL Nan Ding Google EMAIL Dale Schuurmans University of Alberta EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In particular, we investigated three multilabel text classification data sets, Enron, WIPO and Reuters, obtained from https://sites. google.com/site/hrsvmproject/datasets-hier (see Table 1 for details). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits, beyond referring to a 'test set'. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions LBFGS and a bundle method but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In each case, the regularization parameter was simply set to 1. |