32 citations · 96 across the 55 of their papers we have counts for
6 papers · 1 filter
MIXAR: Scaling Autoregressive Pixel-based Language Models to Multiple Languages and Scripts
Chen Hu, Yintao Tai, Antonio Vergari +2
Pixel-based language models are gaining momentum as alternatives to traditional token-based approaches, promising to circumvent tokenization challenges. However, the inherent perce…
SEMMA: A Semantic Aware Knowledge Graph Foundation Model
Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri +4
Knowledge Graph Foundation Models (KGFMs) have shown promise in enabling zero-shot reasoning over unseen graphs by learning transferable patterns. However, most existing KGFMs rely…
Logically Consistent Language Models via Neuro-Symbolic Integration
Diego Calanzone, Stefano Teso, Antonio Vergari
Large language models (LLMs) are a promising venue for natural language understanding and generation. However, current LLMs are far from reliable: they are prone to generating non-…
PIXAR: Auto-Regressive Language Modeling in Pixel Space
Yintao Tai, Xiyang Liao, Alessandro Suglia +1
Recent work showed the possibility of building open-vocabulary large language models (LLMs) that directly operate on pixel representations. These models are implemented as autoenco…
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games
Alessandro Suglia, Yonatan Bisk, Ioannis Konstas +4
Guessing games are a prototypical instance of the "learning by interacting" paradigm. This work investigates how well an artificial agent can benefit from playing guessing games wh…
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games
Alessandro Suglia, Antonio Vergari, Ioannis Konstas +4
In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual repres…