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Alberto Testoni

9 accepted papers

2025

From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions

Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos, Lucas Weber, Alberto Testoni, Marzieh Fadaee +2
ACL 2025long

Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also able to effectively collaborate over long-term interactions? To investigate this, we introduce MemoryCode, a synthetic m…

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2025

LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks

Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt +14
ACL 2025short

There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reproducibility in the case of proprietary models. We provide JUDGE-BENCH, an extensible collection of 20 NLP datasets with hum…

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2025

Playpen: An Environment for Exploring Learning From Dialogue Game Feedback

Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Moment{\`e}, Luca Franceschetti +10
EMNLP 2025

Interaction between learner and feedback-giver has come into focus recently for post-training of Large Language Models (LLMs), through the use of reward models that judge the appropriateness of a model’s response. In this paper, we investigate whether Dialogue Games—goal-directed and rule-governed a

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2025

RAcQUEt: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs

Alberto Testoni, Barbara Plank, Raquel Fern{\'a}ndez
EMNLP 2025

Ambiguity resolution is key to effective communication. While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear. In this work, we examine referential ambiguity in image-based que

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2024

Don’t Buy it! Reassessing the Ad Understanding Abilities of Contrastive Multimodal Models

Anna Bavaresco, Alberto Testoni, Raquel Fernández
ACL 2024short

Image-based advertisements are complex multimodal stimuli that often contain unusual visual elements and figurative language. Previous research on automatic ad understanding has reported impressive zero-shot accuracy of contrastive vision-and-language models (VLMs) on an ad-explanation retrieval tas…

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2024

Learning to Ask Informative Questions: Enhancing LLMs with Preference Optimization and Expected Information Gain

Davide Mazzaccara, Alberto Testoni, Raffaella Bernardi
EMNLP 2024finding

Questions are essential tools for acquiring the necessary information to complete information-seeking tasks. However, large language models (LLMs), especially open-source models, often perform poorly in generating informative questions, as measured by expected information gain (EIG). In this paper,…

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2024

Naming, Describing, and Quantifying Visual Objects in Humans and LLMs

Alberto Testoni, Juell Sprott, Sandro Pezzelle
ACL 2024short

While human speakers use a variety of different expressions when describing the same object in an image, giving rise to a distribution of plausible labels driven by pragmatic constraints, the extent to which current Vision & Language Large Language Models (VLLMs) can mimic this crucial feature of la…

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2022

ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments

Michael Hanna, Federico Pedeni, Alessandro Suglia, Alberto Testoni, Raffaella Bernardi
COLING 2022main

Artificial agents are nowadays challenged to perform embodied AI tasks. To succeed, agents must understand the meaning of verbs and how their corresponding actions transform the surrounding world. In this work, we propose ACT-Thor, a novel controlled benchmark for embodied action understanding. We u…

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2021

Looking for Confirmations: An Effective and Human-Like Visual Dialogue Strategy

Alberto Testoni, Raffaella Bernardi
EMNLP 2021main

Generating goal-oriented questions in Visual Dialogue tasks is a challenging and longstanding problem. State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effective strategy and sound unnatural to humans. Inspired by the cognitive literature o…

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