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.
Learning of Structurally Unambiguous Probabilistic Grammars
Authors: Dolav Nitay, Dana Fisman, Michal Ziv-Ukelson9170-9178
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,903 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,215 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 | As a proof-of-concept, in Section 6 we exemplify our algorithm by applying it to a small data-set of genomic data. |
| Researcher Affiliation | Academia | Dolav Nitay*, Dana Fisman, Michal Ziv-Ukelson Ben Gurion University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Learn CMTA(T, C, H, B). ... Algorithm 5 Extract CMTA |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper mentions using 'a small data-set of genomic data' and an 'MDR dataset' for demonstration, but it does not provide concrete access information (e.g., a link, DOI, or specific citation with author and year for the dataset) to make it publicly available. |
| Dataset Splits | No | The paper mentions applying the algorithm to 'genomic data' and 'gene-cluster grammars' as a demonstration, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for running its experiments or demonstrations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, PyTorch, specific libraries or solvers) used for its implementation or experiments. |
| Experiment Setup | No | The paper describes a demonstration on genomic data, showing a learned PCFG. However, it does not provide specific details about the experimental setup, such as hyperparameters, optimizer settings, training configurations, or other system-level settings for the learning algorithm. |