Structured Voronoi Sampling
Authors: Afra Amini, Li Du, Ryan Cotterell
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an experimental setup where the reference distribution is known, we show that the empirical distribution of SVS samples is closer to the reference distribution compared to alternative sampling schemes. |
| Researcher Affiliation | Academia | Afra Amini1 Li Du2 Ryan Cotterell1 1ETH Zürich 2Johns Hopkins University {afra.amini, ryan.cotterell}@inf.ethz.ch leodu@cs.jhu.edu |
| Pseudocode | Yes | Algorithm 1 HMC, Algorithm 2 Langevin Dynamics, Algorithm 3 MUCOLA, Algorithm 4 Structured Voronoi Sampling, Algorithm 5 REFRACTREFLECT, Algorithm 6 Find Discontinuity |
| Open Source Code | Yes | https://github.com/Afra Amini/svs |
| Open Datasets | Yes | The underlying LM is a finetuned GPT-210 on E2E dataset [34]; see App. G for dataset statistics. This dataset is made available under the CC BY-SA 4.0 license. |
| Dataset Splits | Yes | Table 2: Number of restaurant reviews in each split and food type. train 2929... valid 1489... test 492... |
| Hardware Specification | Yes | All experiments are done on a single A100-40GB GPU. All classifiers are trained and tested on a single gtx_1080_ti GPU with approximately 2 hours of total computational budget. |
| Software Dependencies | No | The paper mentions using a 'gpt2 checkpoint from the Huggingface library [47]' but does not specify version numbers for the Huggingface library itself or other core software dependencies like Python or PyTorch, which would be necessary for full reproducibility. |
| Experiment Setup | Yes | Hyperparameters for each experiment are reported in Table 4. Following prior work [46], in algorithms based on Langevin dynamics, we apply an exponential decay to the step size by decreasing it to 0.05 after 500 steps. In all settings, we take 500 burn-in steps. |