Plug-and-Play Conversational Models

By Andrea Madotto, Etsuko Ishii, Zhaojiang Lin, Sumanth Dathathri, Pascale Fung. 2020

58 citations recorded by the source corpus.

There has been considerable progress made towards conversational models that generate coherent and fluent responses; however, this often involves training large language models on large dialogue datasets, such as Reddit. These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model. In this paper, we first propose and evaluate plug-and-play methods for controllable response generation, which does not require dialogue specific datasets and does not rely on fine-tuning a large model. While effective, the decoding procedure induces considerable computational overhead, rendering the conversational model unsuitable for interactive usage. To overcome this, we introduce an approach that does not require further computation at decoding time, while also does not require any fine-tuning of a large language model. We demonstrate, through extensive automatic and human evaluation, a high degree of control over the generated conversational responses with regard to multiple desired attributes, while being fluent.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Challenges in Detoxifying Language Models (2021, methodology): “PPLM hyperparameters are tuned similar to Madotto et al. (2020), and we refer to Appendix A.2 for additional details.”
GeDi: Generative Discriminator Guided Sequence Generation (2020, background): “…generation can be categorized broadly into two categories: training or finetuning a model directly for controllable generation (Chan et al., 2021; Madotto et al., 2020; Keskar et al., 2019; Ziegler et al., 2019; Rajani et al., 2019; Fan et al., 2018; Ficler and Goldberg, 2017; Yu et al., 2017;…”
DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts (2021, neutral): “We hypothesize that it is easier to derail a positive prompt with negativity than turn something negative into something positive; but to human readers, these negative continuations may be unexpected (a similar observation was made in previous work; Madotto et al., 2020).”
Automatically Exposing Problems with Neural Dialog Models (2021, background): “Alternatively, to utilize the high-quality pre-trained language model quality, Dathathri et al. (2020); Madotto et al. (2020) propose to perturb token distributions towards a speciﬁc attribute with residual adapters (Houlsby et al., 2019).”
Linguistic calibration through metacognition: aligning dialogue agent responses with expected correctness (2020, methodology): “Using larger state-of-the-art conversational architectures, Smith et al. (2020b); Madotto et al. (2020) compared several methods to achieve control in conversation; here, we use the simple method of training attribute-speciﬁc con-trol tokens that was the most effective in Smith et al. (2020b) for a…”
VScript: Controllable Script Generation with Visual Presentation (2022, unknown): “Controllable generation systems (Keskar et al., 2019; Dathathri et al., 2019; Madotto et al., 2020) can help to allow the customization of scripts based on user preferences.”