---
title: "Human vs LLM in Conversational Repair Annotation — Ngo 2026"
description: "Other-Initiated Repair (OIR) happens when a listener flags a problem with the previous turn and the original speaker then fixes it."
authors: ["Anh Ngo", "Nicolas Rollet", "Catherine Pelachaud", "Chloé Clavel"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.547/"
doi: "10.63317/32stceuq67kb"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ngo-2026-human-vs-llm-conversational-repair/"
---
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# Human vs LLM in Conversational Repair Annotation: A New Resource and Comparative Study

**Authors:** Anh Ngo, Nicolas Rollet, [Catherine Pelachaud](https://md.research.elamur.ai/free/en/authors/catherine-pelachaud/), [Chloé Clavel](https://md.research.elamur.ai/free/en/authors/chloe-clavel/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.547/) · [PDF](https://aclanthology.org/2026.lrec-1.547.pdf) · [DOI 10.63317/32stceuq67kb](https://doi.org/10.63317/32stceuq67kb) · [OpenAlex](https://openalex.org/W7160115729)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Other-Initiated Repair (OIR) happens when a listener flags a problem with the previous turn and the original speaker then fixes it. Handling OIR well matters for dialogue systems that must cope with misunderstandings, but annotated data usable for computational work are scarce. CABB-S was essentially the only earlier resource. The authors add an OIR annotation layer to NOXI, a French corpus of expert–novice conversations, with guidelines and a multi-stage validation protocol, and report considerable inter-annotator reliability. Annotators work with a functional "segment" unit and a decision tree grounded in Conversation Analysis. They mark the trouble source, repair initiation and repair solution, and assign a type to each initiation. The study then turns this decision tree into a structured Chain-of-Thought prompt to test LLMs on NOXI and on the Dutch, task-oriented CABB-S. Agreement between models and humans was weak, especially in spotting where trouble is signalled. Model-written explanations show that LLMs follow surface wording instead of sequential context and build convincing but wrong justifications. This is a caution for automating the annotation of complex interactional phenomena.

## Key points
- **NOXI**: about 7 hours of French dyadic conversations in which one speaker is an expert and the other a novice. It has word-level transcripts made by humans and by ASR. This work adds a new OIR annotation layer and guidelines.
- **CABB-S**: an already-annotated subset of the Dutch CABB corpus (Rasenberg et al., 2022), about 5 hours long. Pairs complete an object-matching task and swap director and matcher roles. Its transcripts come with predefined TCU boundaries.
- **Coding scheme**: adapted from Rasenberg et al. (2022), which builds on Kendrick (2015). Using a four-step decision tree, each repair initiation is assigned one of three types: restricted offer, restricted request, or open request. Trouble sources are also given a category, such as mishearing.
- **LLM annotation prompt**: for each detected sequence, the model returns JSON with a reasoning text and a confidence score for every decision step. A second prompt compares two annotations and labels the disagreement type: sequence identification, boundary dispute, repair-initiation type or component identification. It then recommends which annotation fits the OIR definition better.
- **Main finding**: LLM–human alignment is weak on both corpora, with trouble signalling the weakest step. Models lean on lexical cues and produce plausible but misleading narratives.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ngo2026human,
  title = {{Human vs LLM in Conversational Repair Annotation: A New Resource and Comparative Study}},
  author = {Ngo, Anh and Rollet, Nicolas and Pelachaud, Catherine and Clavel, Chloé},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/32stceuq67kb},
  url = {https://doi.org/10.63317/32stceuq67kb}
}
```

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