Papers by Irina Proskurina
HISTOIRESMORALES: A French Dataset for Assessing Moral Alignment (2025.naacl-long)
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Thibaud Leteno, Irina Proskurina, Antoine Gourru, Julien Velcin, Charlotte Laclau, Guillaume Metzler, Christophe Gravier
| Challenge: | HistoiresMorales is a dataset based on moralStories in French . it is based upon annotations of moral values within the dataset . |
| Approach: | They propose a dataset in French that aims to align language models with moral values . they use annotations to ensure their alignment with French norms . |
| Outcome: | The proposed dataset guarantees grammatical accuracy and adaptation to the French cultural context. |
Acceptability Judgements via Examining the Topology of Attention Maps (2022.findings-emnlp)
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Daniil Cherniavskii, Eduard Tulchinskii, Vladislav Mikhailov, Irina Proskurina, Laida Kushnareva, Ekaterina Artemova, Serguei Barannikov, Irina Piontkovskaya, Dmitri Piontkovski, Evgeny Burnaev
| Challenge: | Acceptability judgments are a key component of generative linguistics, but their ability to judge grammatical acceptability has not been explored. |
| Approach: | They propose to exploit the geometric properties of the attention graph to evaluate the grammatical acceptability of sentences using topological data analysis. |
| Outcome: | The proposed approach outperforms nine statistical and Transformer LM baselines on the BLiMP benchmark and the human-level performance on the same benchmark. |
When Quantization Affects Confidence of Large Language Models? (2024.findings-naacl)
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| Challenge: | Existing studies have shown that quantization compromises performance and exacerbates biases in Large Language Models. |
| Approach: | They propose an explanation for quantization loss based on confidence levels . they propose a range of efficient compression and acceleration methods including quan-tization . |
| Outcome: | The proposed methods show that quantization decreases confidence regarding true labels and that it exacerbates biases across different scales. |