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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Understanding Robust Generalization in Learning Regular Languages
Authors: Soham Dan, Osbert Bastani, Dan Roth
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we implement the compositional strategy via an auxiliary task where the goal is to predict the intermediate states visited by the DFA when parsing a string. Our empirical results support our hypothesis, showing that auxiliary tasks can enable robust generalization. |
| Researcher Affiliation | Academia | 1Department of Computer and Information Science, University of Pennsylvania. |
| Pseudocode | No | The paper describes algorithms and methods but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing open-source code or provide links to a code repository. |
| Open Datasets | No | The paper describes a process for synthetically generating data using edge Markov chains based on DFAs (e.g., 'We construct an edge Markov chain e MCid to generate training examples...'), but it does not use or provide concrete access information for a publicly available, pre-existing dataset. |
| Dataset Splits | Yes | We use N + train = 1600 positive and N train = 1600 negative train examples, N + dev = N dev = 200 dev examples, and use N + test = 2000 positive and N test = 2000 negative examples for each of the i.d. and o.o.d. test sets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'RNN with LSTM cells' and 'stochastic gradient descent (SGD)' but does not specify any software platforms (e.g., PyTorch, TensorFlow) or library versions with specific version numbers. |
| Experiment Setup | Yes | We use an RNN with LSTM cells, with an embedding dimension of 50 and a hidden layer with dimension 50, optimized using stochastic gradient descent (SGD) with a learning rate of 0.01 2. |