57 citations · 60 across the 8 of their papers we have counts for
11 papers
How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects
Leonardo Bertolazzi, Sandro Pezzelle, Raffaella Bernardi
Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning problem influences judgments regarding…
How Well Do Vision-Language Models Understand Movies?
Emmanouil Zaranis, António Farinhas, Saul Santos +28
Vision-language models (VLMs) can increasingly handle extended contexts and answer questions about long videos--but can they understand the narrative structure of movies as humans…
Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM
Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2
Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various L…
Dealing with Semantic Underspecification in Multimodal NLP
Sandro Pezzelle
Intelligent systems that aim at mastering language as humans do must deal with its semantic underspecification, namely, the possibility for a linguistic signal to convey only part…
A Psycholinguistic Analysis of BERT's Representations of Compounds
Lars Buijtelaar, Sandro Pezzelle
This work studies the semantic representations learned by BERT for compounds, that is, expressions such as sunlight or bodyguard. We build on recent studies that explore semantic i…
Some of Them Can be Guessed! Exploring the Effect of Linguistic Context in Predicting Quantifiers
Sandro Pezzelle, Shane Steinert-Threlkeld, Raffaela Bernardi +1
We study the role of linguistic context in predicting quantifiers (`few', `all'). We collect crowdsourced data from human participants and test various models in a local (single-se…
Comparatives, Quantifiers, Proportions: A Multi-Task Model for the Learning of Quantities from Vision
Sandro Pezzelle, Ionut-Teodor Sorodoc, Raffaella Bernardi
The present work investigates whether different quantification mechanisms (set comparison, vague quantification, and proportional estimation) can be jointly learned from visual sce…
FOIL it! Find One mismatch between Image and Language caption
Ravi Shekhar, Sandro Pezzelle, Yauhen Klimovich +4
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension o…
Pay Attention to Those Sets! Learning Quantification from Images
Ionut Sorodoc, Sandro Pezzelle, Aurélie Herbelot +2
Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and le…
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision
Sandro Pezzelle, Marco Marelli, Raffaella Bernardi
People can refer to quantities in a visual scene by using either exact cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few, most, all). In humans, these two…
The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou +6
We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative…