# Alquist 4.0: Towards Social Intelligence Using Generative Models and Dialogue Personalization

arXiv: https://arxiv.org/abs/2109.07968 · HTML: https://mlsanity.com/paper/2109.07968/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 34 references that are in the map.
- First posted on arXiv 16 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Advances in Pre-Training Distributed Word Representations](https://mlsanity.com/paper/1712.09405/) (2017)
- [DOC: Deep Open Classification of Text Documents](https://mlsanity.com/paper/1709.08716/) (2017)
- [Dialogue Response Ranking Training with Large-Scale Human Feedback Data](https://mlsanity.com/paper/2009.06978/) (2020)
- [Out-of-Domain Detection for Low-Resource Text Classification Tasks](https://mlsanity.com/paper/1909.05357/) (2019)
- [Likelihood Ratios and Generative Classifiers for Unsupervised Out-of-Domain Detection In Task Oriented Dialog](https://mlsanity.com/paper/1912.12800/) (2019)
- [Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations](https://mlsanity.com/paper/2008.12348/) (2020)
- [OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation](https://mlsanity.com/paper/2104.02484/) (2021)
- [Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph](https://mlsanity.com/paper/2011.03261/) (2020)
- [Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation](https://mlsanity.com/paper/1905.10247/) (2019)
- [Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models](https://mlsanity.com/paper/2011.03259/) (2020)
- [Alquist: The Alexa Prize Socialbot](https://mlsanity.com/paper/1804.06705/) (2018)

## Built on by (most cited first, 9 of 9)

- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [Improving Bot Response Contradiction Detection via Utterance Rewriting](https://mlsanity.com/paper/2207.11862/) (2022)
- [A Systematic Evaluation of Response Selection for Open Domain Dialogue](https://mlsanity.com/paper/2208.04379/) (2022)
- [DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines](https://mlsanity.com/paper/2212.10557/) (2022)
- [The Wizard of Curiosities: Enriching Dialogues with Fun Facts](https://mlsanity.com/paper/2309.11283/) (2023)
- [Alquist 5.0: Dialogue Trees Meet Generative Models. A Novel Approach for Enhancing SocialBot Conversations](https://mlsanity.com/paper/2310.16119/) (2023)
- [Metric Learning and Adaptive Boundary for Out-of-Domain Detection](https://mlsanity.com/paper/2204.10849/) (2022)
- [Flowstorm: Open-Source Platform with Hybrid Dialogue Architecture](https://mlsanity.com/paper/2212.09377/) (2022)
- [Let's Get Personal: Personal Questions Improve SocialBot Performance in the Alexa Prize](https://mlsanity.com/paper/2303.04953/) (2023)

## Abstract (arXiv)

The open domain-dialogue system Alquist has a goal to conduct a coherent and engaging conversation that can be considered as one of the benchmarks of social intelligence. The fourth version of the system, developed within the Alexa Prize Socialbot Grand Challenge 4, brings two main innovations. The first addresses coherence, and the second addresses the engagingness of the conversation. For innovations regarding coherence, we propose a novel hybrid approach combining hand-designed responses and a generative model. The proposed approach utilizes hand-designed dialogues, out-of-domain detection, and a neural response generator. Hand-designed dialogues walk the user through high-quality conversational flows. The out-of-domain detection recognizes that the user diverges from the predefined flow and prevents the system from producing a scripted response that might not make sense for unexpected user input. Finally, the neural response generator generates a response based on the context of the dialogue that correctly reacts to the unexpected user input and returns the dialogue to the boundaries of hand-designed dialogues. The innovations for engagement that we propose are mostly inspired by the famous exploration-exploitation dilemma. To conduct an engaging conversation with the dialogue partners, one has to learn their preferences and interests -- exploration. Moreover, to engage the partner, we have to utilize the knowledge we have already learned -- exploitation. In this work, we present the principles and inner workings of individual components of the open-domain dialogue system Alquist developed within the Alexa Prize Socialbot Grand Challenge 4 and the experiments we have conducted to evaluate them.