Papers by Constanza Fierro
How Do Multilingual Language Models Remember Facts? (2025.findings-acl)
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| Challenge: | Prior research has focused on English monolingual models, but how these mechanisms generalize to non-English languages remains unexplored. |
| Approach: | They analyze three multilingual LLMs to find out how they can generalize recall mechanisms . they find that subject enrichment is language-independent, object extraction is language dependent . |
| Outcome: | The proposed model performs better in multilingual contexts than in English models . the model is more efficient in multi-lingual context, but it is more complex in multilinguistic models compared to English models. |
Defining Knowledge: Bridging Epistemology and Large Language Models (2024.emnlp-main)
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| Challenge: | Existing literature on large language models (LLMs) define knowledge as a fact if it correctly completes a cloze sentence . but the predictions of semantically equivalent clozing sentences are inconsistent . |
| Approach: | They review standard definitions of knowledge in epistemology and formalize interpretations applicable to LLMs. |
| Outcome: | The authors compare the preferences of philosophers and computer scientists in terms of knowledge definitions and evaluation protocols for testing knowledge in accordance with the most relevant definitions. |
Learning to Plan and Generate Text with Citations (2024.acl-long)
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Constanza Fierro, Reinald Kim Amplayo, Fantine Huot, Nicola De Cao, Joshua Maynez, Shashi Narayan, Mirella Lapata
| Challenge: | Large language models (LLMs) are increasingly useful in information-seeking scenarios, ranging from answering simple questions to generating responses to search-like queries. |
| Approach: | They propose to use plan-based models to improve faithfulness, grounding, and controllability of generated content and its organization. |
| Outcome: | The proposed models improve faithfulness, grounding, and controllability of generated content and its organization. |
𝜇PLAN: Summarizing using a Content Plan as Cross-Lingual Bridge (2024.eacl-long)
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Fantine Huot, Joshua Maynez, Chris Alberti, Reinald Kim Amplayo, Priyanka Agrawal, Constanza Fierro, Shashi Narayan, Mirella Lapata
| Challenge: | Recent advances in abstractive summarization have focused on English, but more recently, with the advent of large pre-trained models, the task is becoming more complex. |
| Approach: | They propose an approach to cross-lingual summarization that uses an intermediate planning step as a cross-linguistic bridge. |
| Outcome: | The proposed approach achieves state-of-the-art in terms of informativeness and faithfulness on the XWikis dataset. |
MuLan: A Study of Fact Mutability in Language Models (2024.naacl-short)
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| Challenge: | Pretrained and large language models encode factual knowledge, but factual information changes over time and mutates with the passage of time. |
| Approach: | They propose to use a model to evaluate the ability of English language models to anticipate time-contingency by comparing their models to a benchmark model. |
| Outcome: | The proposed model can predict the president of a country or the winner of sa championship in time, but it is difficult to update them due to their mutability. |
Factual Consistency of Multilingual Pretrained Language Models (2022.findings-acl)
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| Challenge: | Recent work shows that monolingual English language models fill-in-the-blank differently for paraphrases describing the same fact. |
| Approach: | They propose a resource to analyze consistency of English language models . they find that mBERT is as inconsistent as English BERT in paraphrases . |
| Outcome: | The proposed model is as inconsistent as English BERT in English paraphrases, but it is more so for all the other 45 languages. |
Challenges and Strategies in Cross-Cultural NLP (2022.acl-long)
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Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, Anders Søgaard
| Challenge: | Various efforts have been made to accommodate linguistic diversity and serve speakers of many different languages. |
| Approach: | They propose a framework to examine cultural differences in NLP to better serve users . they argue that cultural knowledge, preferences and values can affect NLP practices . |
| Outcome: | The proposed framework examines how cultural knowledge, preferences and values can affect NLP practices. |