Controllable Text Generation with Neurally-Decomposed Oracle
Authors: Tao Meng, Sidi Lu, Nanyun Peng, Kai-Wei Chang
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments conducted on two tasks: (1) text generation with lexical constraints and (2) machine translation with formality control demonstrate that our framework efficiently guides the base model towards the given control factors while maintaining high generation quality. |
| Researcher Affiliation | Academia | Tao Meng University of California, Los Angeles tmeng@cs.ucla.edu Sidi Lu University of California, Los Angeles sidilu@cs.ucla.edu Nanyun Peng University of California, Los Angeles violetpeng@cs.ucla.edu Kai-Wei Chang University of California, Los Angeles kwchang@cs.ucla.edu |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code can be found at https://github.com/Mt Some Three/constrDecoding. |
| Open Datasets | Yes | For unsupervised LCG, we follow the settings in POINTER (Zhang et al., 2020) and conduct our experiments on Yelp! Review and News dataset. [...] For supervised LCG, we evaluate the proposed method on Common Gen (Lin et al., 2020). [...] We conduct our experiments on Fisher and CALLHOME Spanish-English Speech Translation Corpus (Post et al., 2013) |
| Dataset Splits | Yes | For training, it contains 32,651 unique key concepts (i.e. the constraints) with 67,389 completed sequences in total. It also contains a validation set with 993 concepts and 4018 reference sequences. |
| Hardware Specification | No | The provided text does not contain specific hardware specifications (GPU/CPU models, memory, etc.) used for running experiments. Although the author checklist states it's in the appendix, the appendix content is not provided. |
| Software Dependencies | No | The paper does not explicitly list software dependencies with specific version numbers. |
| Experiment Setup | Yes | For each pseudo key, we sample 32 target text with top-p (p = 0.8) random sampling from base model p. [...] We put all details about hyper-parameter settings in the appendix. |