Papers by Valentina Presutti
Latent vs Explicit Knowledge Representation: How ChatGPT Answers Questions about Low-Frequency Entities (2024.lrec-main)
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| Challenge: | In this paper, we compare two different approaches to the free-form Question Answering task. |
| Approach: | They propose to use a new benchmark to test knowledge representations on a dynamic benchmark. |
| Outcome: | The proposed benchmark is particularly challenging and the best model answers only on 50% of the questions. |
KE-MHISTO: Towards a Multilingual Historical Knowledge Extraction Benchmark for Addressing the Long-Tail Problem (2025.findings-acl)
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Arianna Graciotti, Leonardo Piano, Nicolas Lazzari, Enrico Daga, Rocco Tripodi, Valentina Presutti, Livio Pompianu
| Challenge: | Large Language Models struggle when probed for long-tail knowledge due to the inherent sparsity of such data. |
| Approach: | They propose a multilingual benchmark for Entity Linking and Question Answering in the domain of historical music knowledge that provides broader coverage of long-tail knowledge. |
| Outcome: | The proposed model provides broader coverage of long-tail knowledge compared to existing models. |