# Collaborative and Proactive Management of Task-Oriented Conversations

arXiv: https://arxiv.org/abs/2510.05110 · HTML: https://mlsanity.com/paper/2510.05110/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 30 of 30)

- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [Understanding the planning of LLM agents: A survey](https://mlsanity.com/paper/2402.02716/) (2024)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System](https://mlsanity.com/paper/2109.14739/) (2021)
- [GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection](https://mlsanity.com/paper/2111.14592/) (2021)
- [Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context](https://mlsanity.com/paper/1911.10484/) (2019)
- [A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects](https://mlsanity.com/paper/2305.02750/) (2023)
- [Are LLMs All You Need for Task-Oriented Dialogue?](https://mlsanity.com/paper/2304.06556/) (2023)
- [Adding Chit-Chat to Enhance Task-Oriented Dialogues](https://mlsanity.com/paper/2010.12757/) (2020)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning](https://mlsanity.com/paper/2009.08115/) (2020)
- [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://mlsanity.com/paper/2210.08817/) (2022)
- [Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention](https://mlsanity.com/paper/1905.12866/) (2019)
- [Structured Fusion Networks for Dialog](https://mlsanity.com/paper/1907.10016/) (2019)
- [AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models](https://mlsanity.com/paper/2102.05126/) (2021)
- [LAVA: Latent Action Spaces via Variational Auto-encoding for Dialogue Policy Optimization](https://mlsanity.com/paper/2011.09378/) (2020)
- [Task-Optimized Adapters for an End-to-End Task-Oriented Dialogue System](https://mlsanity.com/paper/2305.02468/) (2023)
- [Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue System](https://mlsanity.com/paper/2006.06814/) (2020)
- [Multi-Domain Dialogue Acts and Response Co-Generation](https://mlsanity.com/paper/2004.12363/) (2020)
- [BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2205.02471/) (2022)
- [UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues](https://mlsanity.com/paper/2004.14307/) (2020)
- [Domain State Tracking for a Simplified Dialogue System](https://mlsanity.com/paper/2103.06648/) (2021)
- [Alexa, Let's Work Together: Introducing the First Alexa Prize TaskBot Challenge on Conversational Task Assistance](https://mlsanity.com/paper/2209.06321/) (2022)
- [Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog](https://mlsanity.com/paper/2210.08917/) (2022)
- [Evaluating Task-oriented Dialogue Systems: A Systematic Review of Measures, Constructs and their Operationalisations](https://mlsanity.com/paper/2312.13871/) (2023)
- [Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task](https://mlsanity.com/paper/2208.07097/) (2022)
- [Task-Oriented Dialogue with In-Context Learning](https://mlsanity.com/paper/2402.12234/) (2024)
- [Extracting user needs with Chat-GPT for dialogue recommendation](https://mlsanity.com/paper/2310.19303/) (2023)

## Abstract (arXiv)

Task oriented dialogue systems (TOD) complete particular tasks based on user preferences across natural language interactions. Considering the impressive performance of large language models (LLMs) in natural language processing (NLP) tasks, most of the latest TODs are centered on LLMs. While proactive planning is crucial for task completion, many existing TODs overlook effective goal-aware planning. This paper creates a model for managing task-oriented conversations, conceptualized centered on the information state approach to dialogue management. The created model incorporated constructive intermediate information in planning. Initially, predefined slots and text part informational components are created to model user preferences. Investigating intermediate information, critical circumstances are identified. Informational components corresponding to these circumstances are created. Possible configurations for these informational components lead to limited information states. Then, dialogue moves, which indicate movement between these information states and the procedures that must be performed in the movements, are created. Eventually, the update strategy is constructed. The created model is implemented leveraging in-context learning of LLMs. In this model, database queries are created centered on indicated predefined slots and the order of retrieved entities is indicated centered on text part. This mechanism enables passing the whole corresponding entities to the preferences in the order of congruency. Evaluations exploiting the complete test conversations of MultiWOZ, with no more than a domain in a conversation, illustrate maximal inform and success, and improvement compared with previous methods.