# Task-Oriented Dialogue with In-Context Learning

arXiv: https://arxiv.org/abs/2402.12234 · HTML: https://mlsanity.com/paper/2402.12234/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 26 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Large Language Models Can Self-Improve](https://mlsanity.com/paper/2210.11610/) (2022)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [Learning End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1605.07683/) (2016)
- [Rasa: Open Source Language Understanding and Dialogue Management](https://mlsanity.com/paper/1712.05181/) (2017)
- [Conversational Semantic Parsing for Dialog State Tracking](https://mlsanity.com/paper/2010.12770/) (2020)
- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [Zero-Shot Goal-Directed Dialogue via RL on Imagined Conversations](https://mlsanity.com/paper/2311.05584/) (2023)
- [Dialogue Transformers](https://mlsanity.com/paper/1910.00486/) (2019)
- [PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking](https://mlsanity.com/paper/1909.01296/) (2019)
- [No that's not what I meant: Handling Third Position Repair in Conversational Question Answering](https://mlsanity.com/paper/2307.16689/) (2023)
- [Zero-Shot Generalizable End-to-End Task-Oriented Dialog System using Context Summarization and Domain Schema](https://mlsanity.com/paper/2303.16252/) (2023)

## Built on by (most cited first, 8 of 8)

- [Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications](https://mlsanity.com/paper/2406.10300/) (2024)
- [Conversation Routines: A Prompt Engineering Framework for Task-Oriented Dialog Systems](https://mlsanity.com/paper/2501.11613/) (2025)
- [Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue](https://mlsanity.com/paper/2602.22697/) (2026)
- [Unlocking Proactivity in Task-Oriented Dialogue](https://mlsanity.com/paper/2605.22240/) (2026)
- [An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots](https://mlsanity.com/paper/2410.07094/) (2024)
- [Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering](https://mlsanity.com/paper/2508.03719/) (2025)
- [Collaborative and Proactive Management of Task-Oriented Conversations](https://mlsanity.com/paper/2510.05110/) (2025)
- [Following the Flow the Law Requires: Training Voice Agents to Transfer to Unseen Regulated Procedures in Debt Collection](https://mlsanity.com/paper/2610.18881/) (2026)

## Abstract (arXiv)

We describe a system for building task-oriented dialogue systems combining the in-context learning abilities of large language models (LLMs) with the deterministic execution of business logic. LLMs are used to translate between the surface form of the conversation and a domain-specific language (DSL) which is used to progress the business logic. We compare our approach to the intent-based NLU approach predominantly used in industry today. Our experiments show that developing chatbots with our system requires significantly less effort than established approaches, that these chatbots can successfully navigate complex dialogues which are extremely challenging for NLU-based systems, and that our system has desirable properties for scaling task-oriented dialogue systems to a large number of tasks. We make our implementation available for use and further study.