@inproceedings{russodivito-etal-2024-ai,
title = "{AI} vs. Human: Effectiveness of {LLM}s in Simplifying {I}talian Administrative Documents",
author = "Russodivito, Marco and
Ganfi, Vittorio and
Fiorentino, Giuliana and
Oliveto, Rocco",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.91/",
pages = "842--853",
ISBN = "979-12-210-7060-6",
abstract = "This study investigates the effectiveness of Large Language Models (LLMs) in simplifying Italian administrative texts compared to human informants. This research evaluates the performance of several well-known LLMs, including GPT-3.5-Turbo, GPT-4, LLaMA 3, and Phi 3, in simplifying a corpus of Italian administrative documents (s-ItaIst), a representative corpus of Italian administrative texts. To accurately compare the simplification abilities of humans and LLMs, six parallel corpora of a subsection of ItaIst are collected. These parallel corpora were analyzed using both complexity and similarity metrics to assess the outcomes of LLMs and human participants. Our findings indicate that while LLMs perform comparably to humans in many aspects, there are notable differences in structural and semantic changes. The results of our study underscore the potential and limitations of using AI for administrative text simplification, highlighting areas where LLMs need improvement to achieve human-level proficiency."
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<abstract>This study investigates the effectiveness of Large Language Models (LLMs) in simplifying Italian administrative texts compared to human informants. This research evaluates the performance of several well-known LLMs, including GPT-3.5-Turbo, GPT-4, LLaMA 3, and Phi 3, in simplifying a corpus of Italian administrative documents (s-ItaIst), a representative corpus of Italian administrative texts. To accurately compare the simplification abilities of humans and LLMs, six parallel corpora of a subsection of ItaIst are collected. These parallel corpora were analyzed using both complexity and similarity metrics to assess the outcomes of LLMs and human participants. Our findings indicate that while LLMs perform comparably to humans in many aspects, there are notable differences in structural and semantic changes. The results of our study underscore the potential and limitations of using AI for administrative text simplification, highlighting areas where LLMs need improvement to achieve human-level proficiency.</abstract>
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%0 Conference Proceedings
%T AI vs. Human: Effectiveness of LLMs in Simplifying Italian Administrative Documents
%A Russodivito, Marco
%A Ganfi, Vittorio
%A Fiorentino, Giuliana
%A Oliveto, Rocco
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F russodivito-etal-2024-ai
%X This study investigates the effectiveness of Large Language Models (LLMs) in simplifying Italian administrative texts compared to human informants. This research evaluates the performance of several well-known LLMs, including GPT-3.5-Turbo, GPT-4, LLaMA 3, and Phi 3, in simplifying a corpus of Italian administrative documents (s-ItaIst), a representative corpus of Italian administrative texts. To accurately compare the simplification abilities of humans and LLMs, six parallel corpora of a subsection of ItaIst are collected. These parallel corpora were analyzed using both complexity and similarity metrics to assess the outcomes of LLMs and human participants. Our findings indicate that while LLMs perform comparably to humans in many aspects, there are notable differences in structural and semantic changes. The results of our study underscore the potential and limitations of using AI for administrative text simplification, highlighting areas where LLMs need improvement to achieve human-level proficiency.
%U https://aclanthology.org/2024.clicit-1.91/
%P 842-853
Markdown (Informal)
[AI vs. Human: Effectiveness of LLMs in Simplifying Italian Administrative Documents](https://aclanthology.org/2024.clicit-1.91/) (Russodivito et al., CLiC-it 2024)
ACL