3 citations · 5 across the 24 of their papers we have counts for
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Language, Language Models, and What We're Talking About
Malvina Nissim
Language models are commonly discussed as technical artefacts, but they are obviously shaped by the linguistic worlds conveyed by data during their training. Using Italian language…
Stranger, Fan, or Peer? A Systematic Study on the Role of Interlocutor in Persona-Based Dialogue Generation
Daniela Occhipinti, Malvina Nissim, Marco Guerini
Persona-based dialogue systems are usually conditioned on speaker biography, but dialogues involve at least two participants, and who has access to whose biography can vary across…
Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Models
Sara Candussio, Daniel Scalena, Luca Bortolussi +3
Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test the robustness of low- and hi…
"Don't Say It!": Constraints, Compliance, and Communication when Language Models Play Taboo
Sara Candussio, Francesca Padovani, Daniel Scalena +1
The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple task combines strict lexical…
Puzzled By ChatGPT? No more! A Jigsaw Puzzle to Promote AI Literacy and Awareness
Francesca Padovani, Malvina Nissim
The rapid adoption of Generative AI, including LLM-based chatbots like ChatGPT, has highlighted the need for accessible ways to support public understanding and AI literacy. To add…
ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts
Sara Nabhani, Federico Pianzola, Khalid Al-Khatib +1
Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools for persuasion, their specif…
Corpus Prevalence of Multiple-Choice Question Options
Leonidas Zotos, Hedderik van Rijn, Malvina Nissim
In recent years, corpus-driven AI methods, such as Large Language Models (LLMs), have seen widespread use in education. While on the surface their abilities look promising for task…
TACLer: Tailored Curriculum Reinforcement Learning for Efficient Reasoning
Huiyuan Lai, Malvina Nissim
Large Language Models (LLMs) have shown remarkable performance on complex reasoning tasks, especially when equipped with long chain-of-thought (CoT) reasoning. However, eliciting l…
Practising responsibility: Ethics in NLP as a hands-on course
Malvina Nissim, Viviana Patti, Beatrice Savoldi
As Natural Language Processing (NLP) systems become more pervasive, integrating ethical considerations into NLP education has become essential. However, this presents inherent chal…
Challenging the Abilities of Large Language Models in Italian: a Community Initiative
Malvina Nissim, Danilo Croce, Viviana Patti +78
The rapid progress of Large Language Models (LLMs) has transformed natural language processing and broadened its impact across research and society. Yet, systematic evaluation of t…
NLP Methods May Actually Be Better Than Professors at Estimating Question Difficulty
Leonidas Zotos, Ivo Pascal de Jong, Matias Valdenegro-Toro +3
Estimating the difficulty of exam questions is essential for developing good exams, but professors are not always good at this task. We compare various Large Language Model-based m…
When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation
Daniela Occhipinti, Marco Guerini, Malvina Nissim
Endowing dialogue agents with persona information has proven to significantly improve the consistency and diversity of their generations. While much focus has been placed on aligni…
Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement
Gabriele Sarti, Vilém Zouhar, Malvina Nissim +1
Word-level quality estimation (WQE) aims to automatically identify fine-grained error spans in machine-translated outputs and has found many uses, including assisting translators d…
Steering Large Language Models for Machine Translation Personalization
Daniel Scalena, Gabriele Sarti, Arianna Bisazza +2
Large language models have simplified the production of personalized translations reflecting predefined stylistic constraints. However, these systems still struggle when stylistic…
Multidimensional Consistency Improves Reasoning in Language Models
Huiyuan Lai, Xiao Zhang, Malvina Nissim
While Large language models (LLMs) have proved able to address some complex reasoning tasks, we also know that they are highly sensitive to input variation, which can lead to diffe…
QE4PE: Word-level Quality Estimation for Human Post-Editing
Gabriele Sarti, Vilém Zouhar, Grzegorz Chrupała +3
Word-level quality estimation (QE) methods aim to detect erroneous spans in machine translations, which can direct and facilitate human post-editing. While the accuracy of word-lev…
Are You Doubtful? Oh, It Might Be Difficult Then! Exploring the Use of Model Uncertainty for Question Difficulty Estimation
Leonidas Zotos, Hedderik van Rijn, Malvina Nissim
In an educational setting, an estimate of the difficulty of multiple-choice questions (MCQs), a commonly used strategy to assess learning progress, constitutes very useful informat…
A gentle push funziona benissimo: making instructed models in Italian via contrastive activation steering
Daniel Scalena, Elisabetta Fersini, Malvina Nissim
Adapting models to a language that was only partially present in the pre-training data requires fine-tuning, which is expensive in terms of both data and computational resources. A…
Choosy Babies Need One Coach: Inducing Mode-Seeking Behavior in BabyLlama with Reverse KL Divergence
Shaozhen Shi, Yevgen Matusevych, Malvina Nissim
This study presents our submission to the Strict-Small Track of the 2nd BabyLM Challenge. We use a teacher-student distillation setup with the BabyLLaMa model (Timiryasov and Taste…
Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses
Gabriele Sarti, Tommaso Caselli, Malvina Nissim +1
Rebuses are puzzles requiring constrained multi-step reasoning to identify a hidden phrase from a set of images and letters. In this work, we introduce a large collection of verbal…
mCoT: Multilingual Instruction Tuning for Reasoning Consistency in Language Models
Huiyuan Lai, Malvina Nissim
Large language models (LLMs) with Chain-of-thought (CoT) have recently emerged as a powerful technique for eliciting reasoning to improve various downstream tasks. As most research…
Can Model Uncertainty Function as a Proxy for Multiple-Choice Question Item Difficulty?
Leonidas Zotos, Hedderik van Rijn, Malvina Nissim
Estimating the difficulty of multiple-choice questions would be great help for educators who must spend substantial time creating and piloting stimuli for their tests, and for lear…
Multi-property Steering of Large Language Models with Dynamic Activation Composition
Daniel Scalena, Gabriele Sarti, Malvina Nissim
Activation steering methods were shown to be effective in conditioning language model generation by additively intervening over models' intermediate representations. However, the e…
Fine-tuning with HED-IT: The impact of human post-editing for dialogical language models
Daniela Occhipinti, Michele Marchi, Irene Mondella +4
Automatic methods for generating and gathering linguistic data have proven effective for fine-tuning Language Models (LMs) in languages less resourced than English. Still, while th…