# The Adapter-Bot: All-In-One Controllable Conversational Model

arXiv: https://arxiv.org/abs/2008.12579 · HTML: https://mlsanity.com/paper/2008.12579/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 30 of 38)

- [A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models](https://mlsanity.com/paper/2201.05337/) (2022)
- [Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding](https://mlsanity.com/paper/2104.08455/) (2021)
- [BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data](https://mlsanity.com/paper/2106.06169/) (2021)
- [Few-Shot Bot: Prompt-Based Learning for Dialogue Systems](https://mlsanity.com/paper/2110.08118/) (2021)
- [Mitigating Object Hallucination in Large Vision-Language Models via Image-Grounded Guidance](https://mlsanity.com/paper/2402.08680/) (2024)
- [Knowledge-Grounded Dialogue Generation with a Unified Knowledge Representation](https://mlsanity.com/paper/2112.07924/) (2021)
- [Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System](https://mlsanity.com/paper/2305.02468/) (2023)
- [Fusing task-oriented and open-domain dialogues in conversational agents](https://mlsanity.com/paper/2109.04137/) (2021)
- [A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation](https://mlsanity.com/paper/2204.09867/) (2022)
- [Mixture of Soft Prompts for Controllable Data Generation](https://mlsanity.com/paper/2303.01580/) (2023)
- [Assessing Political Prudence of Open-domain Chatbots](https://mlsanity.com/paper/2106.06157/) (2021)
- [Text Revision by On-the-Fly Representation Optimization](https://mlsanity.com/paper/2204.07359/) (2022)
- [Learning to Memorize Entailment and Discourse Relations for Persona-Consistent Dialogues](https://mlsanity.com/paper/2301.04871/) (2023)
- [LLM-based multi-agent poetry generation in non-cooperative environments](https://mlsanity.com/paper/2409.03659/) (2024)
- [Unsupervised Domain Adaptation with Adapter](https://mlsanity.com/paper/2111.00667/) (2021)
- [BotsTalk: Machine-sourced Framework for Automatic Curation of Large-scale Multi-skill Dialogue Datasets](https://mlsanity.com/paper/2210.12687/) (2022)
- [Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation](https://mlsanity.com/paper/2305.13776/) (2023)
- [MidMed: Towards Mixed-Type Dialogues for Medical Consultation](https://mlsanity.com/paper/2306.02923/) (2023)
- [UniDS: A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues](https://mlsanity.com/paper/2110.08032/) (2021)
- [Model Generalization on COVID-19 Fake News Detection](https://mlsanity.com/paper/2101.03841/) (2021)
- [Training and Serving System of Foundation Models: A Comprehensive Survey](https://mlsanity.com/paper/2401.02643/) (2024)
- [Local Explanation of Dialogue Response Generation](https://mlsanity.com/paper/2106.06528/) (2021)
- [Evaluating the Smooth Control of Attribute Intensity in Text Generation with LLMs](https://mlsanity.com/paper/2406.04460/) (2024)
- [Injecting Domain Knowledge in Language Models for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2212.08120/) (2022)
- [Zero-Shot Controlled Generation with Encoder-Decoder Transformers](https://mlsanity.com/paper/2106.06411/) (2021)
- [Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning](https://mlsanity.com/paper/2206.03715/) (2022)
- [Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog](https://mlsanity.com/paper/2212.10008/) (2022)
- [Hierarchical Inductive Transfer for Continual Dialogue Learning](https://mlsanity.com/paper/2203.10484/) (2022)
- [Can Open Domain Question Answering Systems Answer Visual Knowledge Questions?](https://mlsanity.com/paper/2202.04306/) (2022)
- [Unified Conversational Models with System-Initiated Transitions between Chit-Chat and Task-Oriented Dialogues](https://mlsanity.com/paper/2307.01664/) (2023)

## Abstract (arXiv)

Considerable progress has been made towards conversational models that generate coherent and fluent responses by training large language models on large dialogue datasets. These models have little or no control of the generated responses and miss two important features: continuous dialogue skills integration and seamlessly leveraging diverse knowledge sources. In this paper, we propose the Adapter-Bot, a dialogue model that uses a fixed backbone conversational model such as DialGPT (Zhang et al., 2019) and triggers on-demand dialogue skills (e.g., emphatic response, weather information, movie recommendation) via different adapters (Houlsby et al., 2019). Each adapter can be trained independently, thus allowing a continual integration of skills without retraining the entire model. Depending on the skills, the model is able to process multiple knowledge types, such as text, tables, and graphs, in a seamless manner. The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses. At the current stage, we have implemented 12 response styles (e.g., positive, negative etc.), 8 goal-oriented skills (e.g. weather information, movie recommendation, etc.), and personalized and emphatic responses. We evaluate our model using automatic evaluation by comparing it with existing state-of-the-art conversational models, and we have released an interactive system at adapter.bot.ust.hk.