Papers by Antonio Vergari
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games (2020.coling-main)
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Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Existing models fail to learn multi-modal representations, relying on category labels at inference time. |
| Approach: | They propose a "imagination" module that learns context-aware and category-awful latent embeddings without relying on category labels at inference time. |
| Outcome: | The imagination module outperforms state-of-the-art competitors by 8.26% gameplay accuracy in the CompGuessWhat?! benchmark. |
PIXAR: Auto-Regressive Language Modeling in Pixel Space (2024.findings-acl)
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| Challenge: | Recent work shows the possibility of building open-vocabulary large language models that operate on pixel representations. |
| Approach: | They propose a pixel-based autoregressive LLM that performs generative tasks . they propose an adversarial pretraining stage that improves the readability and accuracy of PIXAR by 8.1 on LAMBADA and 8.5 on bAbI. |
| Outcome: | The proposed model performs free-form generative tasks while keeping the number of parameters on par with previous encoder-decoder models. |
SEMMA: A Semantic Aware Knowledge Graph Foundation Model (2025.emnlp-main)
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Arvindh Arun, Sumit Kumar, Mojtaba Nayyeri, Bo Xiong, Ponnurangam Kumaraguru, Antonio Vergari, Steffen Staab
| Challenge: | Existing Knowledge Graph Foundation Models (KGFMs) rely on graph structure, overlooking the rich semantic signals encoded in textual attributes. |
| Approach: | They propose a dual-module KGFM that integrates transferable textual semantics alongside structure to generate relation identifiers. |
| Outcome: | The proposed model outperforms ULTRA and ULtra in fully inductive link prediction in more challenging generalization settings. |
Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility (2026.findings-acl)
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| Challenge: | a recent development of code-LLMs has demonstrated remarkable performance across various software engineering applications. |
| Approach: | They propose a round-trip code execution reasoning task to test round- trip consistency . they use zero-shot prompting, supervised fine-tuning on execution traces and self-reflection mechanisms to evaluate models . |
| Outcome: | The proposed benchmarks show that LLMs struggle with round-trip consistency . the benchmarks lack the internal coherence required for trustworthy code reasoning . |
What can Large Language Models Capture about Code Functional Equivalence? (2025.findings-naacl)
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| Challenge: | SeqCoBench is a benchmark to assess how Code-LLMs can capture code semantics. |
| Approach: | They propose a benchmark to assess how Code-LLMs capture code semantics . they use seqCoBench to evaluate whether they can discern semantically equivalent or different pairs of programs . |
| Outcome: | The proposed benchmarks show that they can capture code semantics better than classical match-based retrieval scores. |
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games (2021.eacl-main)
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Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
| Challenge: | Using guessing games, an artificial agent can learn to perform on novel downstream tasks such as Visual Question Answering (VQA). |
| Approach: | They propose a supervised learning scenario in which an agent learns to mimic successful guessing games and a novel way for an agent to play by itself, called Self-play via Iterated Experience Learning. |
| Outcome: | The proposed model can be applied to a VQA dataset using a supervised learning scenario and a novel way for an agent to play by itself. |