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..
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Authors: Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, Rosanne Liu
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Model samples demonstrate control over a range of topics and sentiment styles, and extensive automated and human annotated evaluations show attribute alignment and fluency. |
| Researcher Affiliation | Collaboration | Sumanth Dathathri CMS, Caltech Andrea Madotto HKUST Janice Lan Uber AI Jane Hung Uber AI Eric Frank Uber AI Piero Molino Uber AI Jason Yosinski Uber AI Rosanne Liu |
| Pseudocode | No | The paper describes the method using mathematical equations and descriptive text, but does not include any clearly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Code for the experiments is available at: https://github.com/uber-research/PPLM. |
| Open Datasets | Yes | The sentiment discriminator here distinguishes sentiment between POSITIVE and NEGATIVE and is trained on the SST-5 dataset (Socher et al., 2013). |
| Dataset Splits | Yes | The sentiment discriminator here distinguishes sentiment between POSITIVE and NEGATIVE and is trained on the SST-5 dataset (Socher et al., 2013). |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware used for running experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using a GPT-2 model and a PyTorch transformer, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Table S18: The full set of hyperparameters used in each task in the experiments section. Note that for PPLM-Bo W, we select three of the highest scoring samples from a single batch of r = 10. For PPLM-Discrim, we get 1 sample per batch, across 3 batches of r = 10. Method Type Attribute Hyperparameters PPLM-Bo W Politics, Legal, Computers, Space, Science, Military m = 3, λkl = 0.01, α = 0.01, γ = 1.5, γgm = 0.9, r = 10, τ = 0.85 PPLM-Bo W Religion m = 3, λkl = 0.01, α = 0.01, γ = 1.5, γgm = 0.8, r = 10, τ = 0.85 PPLM-Discrim POSITIVE, NEGATIVE m = 10, λkl = 0.01, α = 0.03, γ = 1.0, γgm = 0.95, r = 10, τ = 0.9 PPLM-Discrim Detoxicification m = 10, λkl = 0.01, α = 0.02, γ = 1.0, γgm = 0.9, r = 1, τ = 0 |