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Elena Sofia Ruzzetti

7 accepted papers

2025

Position Paper: MeMo: Towards Language Models with Associative Memory Mechanisms

ACL 2025finding

Memorization is a fundamental ability of Transformer-based Large Language Models, achieved through learning. In this position/theory paper, we propose a paradigm shift by designing an architecture to memorize text directly, bearing in mind the principle that memorization precedes learning. We introd…

Cited by 0SourcePDFScholar
2025

Private Memorization Editing: Turning Memorization into a Defense to Strengthen Data Privacy in Large Language Models

ACL 2025long

Large Language Models (LLMs) memorize, and thus, among huge amounts of uncontrolled data, may memorize Personally Identifiable Information (PII), which should not be stored and, consequently, not leaked. In this paper, we introduce Private Memorization Editing (PME), an approach for preventing priva…

2024

A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages

NAACL 2024findings

Reasoning methods, best exemplified by the well-known Chain-of-Thought (CoT), empower the reasoning abilities of Large Language Models (LLMs) by eliciting them to solve complex tasks in a step-by-step manner. Although they are achieving significant success, the ability to deliver multi-step reasonin…

Cited by 11SourcePDFScholar
2024

Investigating the Impact of Data Contamination of Large Language Models in Text-to-SQL translation

ACL 2024findings

Understanding textual description to generate code seems to be an achieved capability of instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there is a severe possibility that this translation ability may be influenced by having seen target textual descriptions and the…

Cited by 12SourcePDFScholar
2023

Exploring Linguistic Properties of Monolingual BERTs with Typological Classification among Languages

EMNLP 2023long findings

The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language. In this paper, we propose a novel standpoint to investigate the above issue: using typological similarities among languages to observe how their re…

Cited by 0SourceScholar
2023

Measuring bias in Instruction-Following models with P-AT

EMNLP 2023long findings

Instruction-Following Language Models (IFLMs) are promising and versatile tools for solving many downstream, information-seeking tasks. Given their success, there is an urgent need to have a shared resource to determine whether existing and new IFLMs are prone to produce biased language interactions…

Cited by 7SourceScholar
2022

Lacking the Embedding of a Word? Look it up into a Traditional Dictionary

ACL 2022findings

Word embeddings are powerful dictionaries, which may easily capture language variations. However, these dictionaries fail to give sense to rare words, which are surprisingly often covered by traditional dictionaries. In this paper, we propose to use definitions retrieved in traditional dictionaries…

Cited by 17SourcePDFScholar