# Dialogue Ontology Relation Extraction via Constrained Chain-of-Thought Decoding

arXiv: https://arxiv.org/abs/2408.02361 · HTML: https://mlsanity.com/paper/2408.02361/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 8 of its 54 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 8 of 8)

- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Chain-of-Thought Reasoning Without Prompting](https://mlsanity.com/paper/2402.10200/) (2024)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve](https://mlsanity.com/paper/2309.13638/) (2023)
- [AutoRE: Document-Level Relation Extraction with Large Language Models](https://mlsanity.com/paper/2403.14888/) (2024)

## Built on by (most cited first, 5 of 5)

- [Post-Training Large Language Models via Reinforcement Learning from Self-Feedback](https://mlsanity.com/paper/2507.21931/) (2025)
- [All Learning Has an Emotional Basis, So Does Task-Oriented Dialogue](https://mlsanity.com/paper/2507.01594/) (2025)
- [METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues](https://mlsanity.com/paper/2604.11427/) (2026)
- [Text-to-SQL Task-oriented Dialogue Ontology Construction](https://mlsanity.com/paper/2507.23358/) (2025)
- [Leveraging Low-Level Symbolic Competences for Unsupervised Grounding in Hallucination Detection](https://mlsanity.com/paper/2609.05025/) (2026)

## Abstract (arXiv)

State-of-the-art task-oriented dialogue systems typically rely on task-specific ontologies for fulfilling user queries. The majority of task-oriented dialogue data, such as customer service recordings, comes without ontology and annotation. Such ontologies are normally built manually, limiting the application of specialised systems. Dialogue ontology construction is an approach for automating that process and typically consists of two steps: term extraction and relation extraction. In this work, we focus on relation extraction in a transfer learning set-up. To improve the generalisation, we propose an extension to the decoding mechanism of large language models. We adapt Chain-of-Thought (CoT) decoding, recently developed for reasoning problems, to generative relation extraction. Here, we generate multiple branches in the decoding space and select the relations based on a confidence threshold. By constraining the decoding to ontology terms and relations, we aim to decrease the risk of hallucination. We conduct extensive experimentation on two widely used datasets and find improvements in performance on target ontology for source fine-tuned and one-shot prompted large language models.