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..
Tractable Control for Autoregressive Language Generation
Authors: Honghua Zhang, Meihua Dang, Nanyun Peng, Guy Van Den Broeck
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the effectiveness of Ge La To on challenging benchmarks for constrained generation: Common Gen (Lin et al., 2020), Yelp!Review (Cho et al., 2019) and News (Zhang et al., 2020); in particular, we focus on Common Gen for detailed analysis. For both unsupervised and supervised settings, Ge La To achieves state-of-the-art performance in terms of various automatic evaluation metrics including BLEU score while guaranteeing 100% constraint satisfaction. Main evaluation results are presented in Table 1. |
| Researcher Affiliation | Academia | Honghua Zhang * 1 Meihua Dang * 1 Nanyun Peng 1 Guy Van den Broeck 1 1Department of Computer Science, University of California, Los Angeles, USA. |
| Pseudocode | Yes | Algorithm 1 Constrained Sampling with Ge La To |
| Open Source Code | Yes | In this section, we demonstrate the effectiveness of Ge La To2 https://github.com/UCLA-StarAI/GeLaTo on challenging benchmarks for constrained generation |
| Open Datasets | Yes | Common Gen (Lin et al., 2020) is a benchmark for constrained generation with lexical constraints... We also evaluate Ge La To on the Yelp!Review (Cho et al., 2019) and the News (Zhang et al., 2020) datasets. |
| Dataset Splits | Yes | For hyper-parameter tuning, we conduct cross-validation on a small subset of the training set and report evaluation results for both validation (dev) and test set. |
| Hardware Specification | Yes | all methods are evaluated on a single NVIDIA A100 GPU with 40 GB memory |
| Software Dependencies | No | The paper mentions the use of 'Juice.jl framework (Dang et al., 2021)' and 'LemmInflect3', but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | Unsupervised Setting: we fine-tune the model for 1 epoch with learning rate = 1e-6. Supervised Setting: for 3 epochs with learning rate = 1e-6. Training HMMs: we train HMMs with the expectation-maximization (EM) algorithm for 40 epochs, and we resample 0.2 million examples for each epoch. Decoding: We adopt beam search to greedily search for x1:n that maximizes p(x1:n | α); we experiment with different beam sizes: 16, 32, 64 and 128. |