# Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT

arXiv: https://arxiv.org/abs/2409.06243 · HTML: https://mlsanity.com/paper/2409.06243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Learning To Retrieve Prompts for In-Context Learning](https://mlsanity.com/paper/2112.08633/) (2021)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://mlsanity.com/paper/2303.08518/) (2023)
- [Zero-Shot Transfer Learning with Synthesized Data for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2005.00891/) (2020)
- [Zero-Shot Dialogue State Tracking via Cross-Task Transfer](https://mlsanity.com/paper/2109.04655/) (2021)
- [From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap](https://mlsanity.com/paper/2004.05827/) (2020)
- [ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?](https://mlsanity.com/paper/2306.01386/) (2023)
- [Zero-shot Generalization in Dialog State Tracking through Generative Question Answering](https://mlsanity.com/paper/2101.08333/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task](https://mlsanity.com/paper/2209.07742/) (2022)

## Built on by (most cited first, 3 of 3)

- [NOEM3A: a Neuro-symbolic Ontology-Enhanced Method for Multi-intent understanding in Mobile Agents](https://mlsanity.com/paper/2511.19780/) (2025)
- [ReacTOD: Bounded Neuro-Symbolic Agentic NLU for Zero-Shot Dialogue State Tracking](https://mlsanity.com/paper/2605.19077/) (2026)
- [GEM: Graph-Enhanced Mixture-of-Experts with ReAct Agents for Dialogue State Tracking](https://mlsanity.com/paper/2605.04449/) (2026)

## Abstract (arXiv)

Traditional dialogue state tracking approaches heavily rely on extensive training data and handcrafted features, limiting their scalability and adaptability to new domains. In this paper, we propose a novel method that leverages inference and in-context learning with ChatGPT for domain transfer in dialogue state tracking, without any parameter updates. By guiding ChatGPT's chain of thought, we enable it to retrieve relevant examples and generalize knowledge to accurately infer dialogue states, solely through inference. Experimental results on the MultiWOZ dataset demonstrate competitive performance and promising generalization across domains. Our parameter-free approach offers a scalable and adaptable solution, opening new research directions in domain transfer learning.