# Plug-and-Play Conversational Models

arXiv: https://arxiv.org/abs/2010.04344 · HTML: https://mlsanity.com/paper/2010.04344/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 30 of 30)

- [DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://mlsanity.com/paper/2105.03023/) (2021)
- [Challenges in Detoxifying Language Models](https://mlsanity.com/paper/2109.07445/) (2021)
- [Few-Shot Bot: Prompt-Based Learning for Dialogue Systems](https://mlsanity.com/paper/2110.08118/) (2021)
- [Attribute Alignment: Controlling Text Generation from Pre-trained Language Models](https://mlsanity.com/paper/2103.11070/) (2021)
- [Multi-Stage Prompting for Knowledgeable Dialogue Generation](https://mlsanity.com/paper/2203.08745/) (2022)
- [Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes](https://mlsanity.com/paper/2104.04039/) (2021)
- [Why is constrained neural language generation particularly challenging?](https://mlsanity.com/paper/2206.05395/) (2022)
- [Improved and Efficient Conversational Slot Labeling through Question Answering](https://mlsanity.com/paper/2204.02123/) (2022)
- [Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation](https://mlsanity.com/paper/2310.14892/) (2023)
- [Focused Prefix Tuning for Controllable Text Generation](https://mlsanity.com/paper/2306.00369/) (2023)
- [Plug-and-Play Document Modules for Pre-trained Models](https://mlsanity.com/paper/2305.17660/) (2023)
- [Protecting Language Models Against Unauthorized Distillation through Trace Rewriting](https://mlsanity.com/paper/2602.15143/) (2026)
- [CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System](https://mlsanity.com/paper/2106.03530/) (2021)
- [Zero-Shot Controlled Generation with Encoder-Decoder Transformers](https://mlsanity.com/paper/2106.06411/) (2021)
- [SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks](https://mlsanity.com/paper/2109.01958/) (2021)
- [Automatically Exposing Problems with Neural Dialog Models](https://mlsanity.com/paper/2109.06950/) (2021)
- [Long-term Control for Dialogue Generation: Methods and Evaluation](https://mlsanity.com/paper/2205.07352/) (2022)
- [Every picture tells a story: Image-grounded controllable stylistic story generation](https://mlsanity.com/paper/2209.01638/) (2022)
- [Nora: The Well-Being Coach](https://mlsanity.com/paper/2106.00410/) (2021)
- [THEaiTRE 1.0: Interactive generation of theatre play scripts](https://mlsanity.com/paper/2102.08892/) (2021)
- [Reconsidering the Past: Optimizing Hidden States in Language Models](https://mlsanity.com/paper/2112.08653/) (2021)
- [Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection](https://mlsanity.com/paper/2203.11399/) (2022)
- [Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models](https://mlsanity.com/paper/2510.08592/) (2025)
- [Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing](https://mlsanity.com/paper/2510.12121/) (2025)
- [Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models](https://mlsanity.com/paper/2110.07331/) (2021)
- [VScript: Controllable Script Generation with Visual Presentation](https://mlsanity.com/paper/2203.00314/) (2022)
- [Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules](https://mlsanity.com/paper/2310.15724/) (2023)
- [Controllable Response Generation for Assistive Use-cases](https://mlsanity.com/paper/2112.02246/) (2021)
- [A Conversational Brain-Artificial Intelligence Interface](https://mlsanity.com/paper/2402.15011/) (2024)
- [A Comparative Study of Controlled Text Generation Systems Using Level-Playing-Field Evaluation Principles](https://mlsanity.com/paper/2605.12395/) (2026)

## Abstract (arXiv)

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.