---
title: "Gradient Descenders at DeSegMa-IT: Leveraging Monolingual"
description: "The authors (team Gradient Descenders) took part in the DeSegMa-IT shared task at… 94.58% accuracy. 26 references, cited by 1 paper here."
authors: ["Tran Phuoc Thanh Nhan", "Bui Hong Son", "Dang Van Thin"]
year: 2026
venue: "Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)"
source: "https://aclanthology.org/2026.evalita-1.6/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nhan-2026-gradient-descenders-desegma-it-leveraging/"
---
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# Gradient Descenders at DeSegMa-IT: Leveraging Monolingual Transformer for LLM-Generated Text Detection and Boundary Identification

**Authors:** [Tran Phuoc Thanh Nhan](https://md.research.elamur.ai/free/en/authors/tran-phuoc-thanh-nhan/), [Bui Hong Son](https://md.research.elamur.ai/free/en/authors/bui-hong-son/) and [Dang Van Thin](https://md.research.elamur.ai/free/en/authors/dang-van-thin/) · **Year:** 2026 · **Venue:** [Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)](https://md.research.elamur.ai/free/en/venues/evalita/) ([Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop 2026](https://md.research.elamur.ai/free/en/venues/evalita/2026/)) · **Publisher:** [CEUR Workshop Proceedings](https://md.research.elamur.ai/free/en/publishers/ceur-workshop-proceedings/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.evalita-1.6/) · [PDF](https://aclanthology.org/2026.evalita-1.6.pdf)

**Citations:** [1](https://md.research.elamur.ai/free/en/papers/nhan-2026-gradient-descenders-desegma-it-leveraging/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The authors (team Gradient Descenders) took part in the DeSegMa-IT shared task at EVALITA 2026, which covers machine-generated text (MGT) in Italian. The task has two parts. Subtask A asks whether a text was written by a human or by an LLM. Subtask B uses texts that begin with human writing and continue with LLM output, and the goal is to find the token where authorship changes.

The team argues that monolingual Italian encoders capture the language's morphology better than multilingual models such as XLM-RoBERTa or mDeBERTa. For Subtask A, they fine-tuned UmBERTo as a sequence classifier. For Subtask B, they cast boundary finding as binary token labelling, using DeBERTa-base-Italian with a linear head. This gives a finer resolution than earlier sentence-level methods.

On the official test sets, the system ranked first in Subtask A and third in Subtask B. The results suggest that compact, language-specific encoders remain strong tools for detecting LLM text in languages other than English.

## Key points
- **Subtask A:** UmBERTo, a RoBERTa-style model pre-trained on the Italian portion of OSCAR, ranked 1st with 94.58% accuracy. Its classifier uses the [CLS] vector and a two-layer MLP (Tanh activation, dropout), trained with cross-entropy loss.
- **Subtask B backbone:** DeBERTa-base-Italian is derived from mDeBERTa-v3-base. It was adapted to Italian by swapping the embedding layer and continuing pre-training on Italian data.
- **Subtask B results:** the system ranked 3rd with an MAE of 62.66, ahead of Nicla by 39.38 and UniTor by 18.94. In the backbone ablation, mDeBERTa-V3 scored 63.34, slightly worse.
- **Limitations:** the system is sensitive to domain shift, predicts each token's label independently, and truncates inputs at 512 tokens.
- **Planned work:** Domain-Adversarial Neural Networks (DANN) for domain robustness, Bi-LSTM layers to model label dependencies, and Longformer or sliding-window inference for longer documents.

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

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works

## Cite

Cite as: Tran Phuoc Thanh Nhan, Bui Hong Son and Dang Van Thin. 2026. Gradient Descenders at DeSegMa-IT: Leveraging Monolingual Transformer for LLM-Generated Text Detection and Boundary Identification. Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026).

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

```bibtex
@inproceedings{nhan2026gradient,
  title = {{Gradient Descenders at DeSegMa-IT: Leveraging Monolingual Transformer for LLM-Generated Text Detection and Boundary Identification}},
  author = {Tran Phuoc Thanh Nhan, Bui Hong Son and Dang {Van Thin}},
  booktitle = {Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)},
  year = {2026},
  url = {https://aclanthology.org/2026.evalita-1.6/}
}
```

- [← previous GoBlueInformatics at #SMM4H-HeaRD 2026: Long-Context Encoders and Generative Biomedical LLMs for Pathological TNM Stage Prediction](https://md.research.elamur.ai/free/en/papers/wei-2026-goblueinformatics-smm4h-heard-2026-long/)
- [next → Grammar as Control: Modular Language Generation for the Long Tail](https://md.research.elamur.ai/free/en/papers/nakashole-2026-grammar-as-control-modular-language/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/13/)

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*Updated 1 November 2026*