Source Themes

Controllable Text Generation To Evaluate Linguistic Abilities of Italian LLMs

State-of-the-art Large Language Models (LLMs) demonstrate exceptional proficiency across diverse tasks, yet systematic evaluations of their linguistic abilities remain limited. This paper addresses this gap by proposing a new evaluation framework …

Fantastic Labels and Where to Find Them: Attention-Based Label Selection for Text-to-Text Classification

Generative language models, particularly adopting text-to-text frameworks, have shown significant success in NLP tasks. While much research has focused on input representations via prompting techniques, less attention has been given to optimizing …

Evaluating Large Language Models via Linguistic Profiling

Large Language Models (LLMs) undergo extensive evaluation against various benchmarks collected in established leaderboards to assess their performance across multiple tasks. However, to the best of our knowledge, there is a lack of comprehensive …

Leveraging Large Language Models for Mobile App Review Feature Extraction

Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews is essential for tasks such as feature prioritization and sentiment …

Linguistic Knowledge Can Enhance Encoder-Decoder Models (If You Let It)

In this paper, we explore the impact of augmenting pre-trained Encoder-Decoder models, specifically T5, with linguistic knowledge for the prediction of a target task. In particular, we investigate whether fine-tuning a T5 model on an intermediate …

T-FREX: A Transformer-based Feature Extraction Method for Mobile App Reviews

Mobile app reviews are a large-scale data source for software-related knowledge generation activities, including software maintenance, evolution and feedback analysis. Effective extraction of features (i.e., functionalities or characteristics) from …

Lost in Labels: An Ongoing Quest to Optimize Text-to-Text Label Selection for Classification

In this paper, we present an evaluation of the influence of label selection on the performance of a Sequence-to-Sequence Transformer model in a classification task. Our study investigates whether the choice of words used to represent classification …

Unmasking the Wordsmith: Revealing Author Identity through Reader Reviews

Traditional genre-based approaches for book recommendations face challenges due to the vague definition of genres. To overcome this, we propose a novel task called Book Author Prediction, where we predict the author of a book based on user-generated …

LangLearn at EVALITA 2023: Overview of the Language Learning Development Task

Language Learning Development (LangLearn) is the EVALITA 2023 shared task on automatic language development assessment, which consists in predicting the evolution of the written language abilities of learners across time. LangLearn is conceived to be …

Tell me how you write and I'll tell you what you read: a study on the writing style of book reviews

Purpose The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies …