---
title: "Teaching LLMs to unveil tendentious implicit — Paci 2026"
description: "Political speakers often slip debatable claims into speeches through presuppositions and implicatures, so they never openly commit to them."
authors: ["Walter Paci", "Lorenzo Gregori", "Alessandro Panunzi"]
year: 2026
venue: "Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics"
source: "https://aclanthology.org/2026.cmcl-1.17/"
doi: "10.63317/4da4mm3sngm7"
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/paci-2026-teaching-llms-unveil-tendentious-implicit/"
---
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# Teaching LLMs to unveil tendentious implicit contents of Italian political communication

**Authors:** Walter Paci, Lorenzo Gregori, Alessandro Panunzi · **Year:** 2026 · **Venue:** Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics · **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.cmcl-1.17/) · [PDF](https://aclanthology.org/2026.cmcl-1.17.pdf) · [DOI 10.63317/4da4mm3sngm7](https://doi.org/10.63317/4da4mm3sngm7) · [OpenAlex](https://openalex.org/W7166539536)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Political speakers often slip debatable claims into speeches through presuppositions and implicatures, so they never openly commit to them. Earlier work on Italian data showed that general-purpose LLMs, used without task training, mostly fail to reconstruct such manipulative meanings. This paper tests whether supervised instruction tuning can close that gap. The authors start from IMPAQTS-PID, a benchmark built on the expert-annotated IMPAQTS corpus of Italian political speeches. They remove its multiple-choice options and turn it into an open generation task: the model receives a speech excerpt with the implicit segment marked by tags and must state the hidden content explicitly. Llama3.1-8B-Instruct and Qwen2.5-7B-Instruct are fine-tuned on 25,000 such pairs. They are compared against zero-shot GPT-4o-mini results from prior work. Three independent annotators rate the outputs on a 0–4 correctness scale, and BERTScore is also examined. The fine-tuned models clearly beat the earlier benchmark results, and both explain presuppositions much better than implicatures. The authors conclude that task-specific pragmatic supervision, not only model scale, drives progress in interpreting implicit meaning.

## Key points
- **IMPAQTS corpus.** It contains 1,403 monologic speeches (about 2 million words) by 150 Italian politicians from 1946 to 2023. The speeches cover six types, from parliamentary speeches to new media statements, and three historical periods. Only non-bona fide true (potentially manipulative) implicit content is annotated, each instance with a class label and an explanation.
- **IMPAQTS-PID dataset.** It has 31,822 instances: 14,925 implicatures and 16,897 presuppositions. The split is 25,000 for training and 6,822 for testing.
- **Fine-tuning setup.** Llama3.1-8B-Instruct and Qwen2.5-7B-Instruct were tuned with an Italian instruction prompt for 3 epochs. Settings: learning rate 2e-5, cosine scheduler, batch size 2, gradient accumulation 8, warmup 0.1.
- **Evaluation.** Each model received 50 manually rated explanations from 3 annotators on a 0–4 scale: 0 = missing output, 4 = fully correct. BERTScore was evaluated as an automatic metric. The baseline is zero-shot GPT-4o-mini from Paci et al. (2025).
- **Main findings.** Fine-tuning substantially improves on prior zero-shot benchmarks, and presuppositions are explained considerably better than implicatures. The work continues Paci (2025), Paci et al. (2025) and the IMPOLS shared task at EVALITA 2026.

*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/)

Topics: [Implicature](https://md.research.elamur.ai/free/en/topics/implicature/) · [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/)

## Cite

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

```bibtex
@inproceedings{paci2026teaching,
  title = {{Teaching LLMs to unveil tendentious implicit contents of Italian political communication}},
  author = {Paci, Walter and Gregori, Lorenzo and Panunzi, Alessandro},
  booktitle = {Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics},
  year = {2026},
  doi = {10.63317/4da4mm3sngm7},
  url = {https://doi.org/10.63317/4da4mm3sngm7}
}
```

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## References

24 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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21. The following tables reports the judgments of each annotator per score. Both partial values on presuppositions and implicatures, and the overall values are reported. gradient_accumulation_steps=8 learning_rate=2e-5 lr_scheduler_type="cosine" warmup_ratio=0.1 [find](https://scholar.google.com/scholar?q=The+following+tables+reports+the+judgments+of+each+annotator+per+score%2E+Both+partial+values+on+presuppositions+and+implicatures%2C+and+the+overall+values+are+reported%2E+gradient%5Faccumulation%5Fsteps%3D8+lear)
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24. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Score Total items annot 1 annot 2 annot 3 Implicatures annot 1 annot 2 annot 3 Presuppositions annot 1 annot 2 annot 3 22 19 24 0 0 0 15 15 13 13 16 13 0 0 0 4 3 6 0 0 0 10 7 8 10 14 10 0 0 0 18 16 18 0 0 0… [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2C+Felix+Wu%2C+Kilian+Q+Weinberger%2C+and+Yoav+Artzi%2E+2019%2E+Bertscore%3A+Score+Total+items+annot+1+annot+2+annot+3+Implicatures+annot+1+annot+2+annot+3+Presuppositions+annot+1+ann)