Papers by Marco Brambilla
Comparing Human and Large Language Model Interpretation of Implicit Information (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are a popular approach for generating text indistinguishable from human-generated language. |
| Approach: | They propose an LLM-based pipeline that builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. |
| Outcome: | The proposed pipeline builds a structured knowledge graph from a context sentence by extracting relational triplets, validating implicit inferences, and analyzing temporal relations. |
Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Semantic sentence embeddings are usually supervisedly built minimizing distances between pairs of embeddable sentences labelled as semantically similar by annotators. |
| Approach: | They propose a language-independent approach to build large datasets of pairs of informal texts weakly similar, without manual human effort, exploiting Twitter’s powerful signals of relatedness: replies and quotes of tweets. |
| Outcome: | The proposed model learns classical Semantic Textual Similarity, and excels on tasks where pairs of sentences are not exact paraphrases. |