Papers by Lucio La Cava
Talking the Talk Does Not Entail Walking the Walk: On the Limits of Large Language Models in Lexical Entailment Recognition (2024.findings-emnlp)
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| Challenge: | Verbs are crucial for expressing actions and relationships between entities, making it essential to properly capture their nuances. |
| Approach: | They propose to use prompting strategies and zero-shot prompting to recognize entailment relations among verbs from two lexical databases, WordNet and HyperLex. |
| Outcome: | The proposed models can tackle the lexical entailment recognition task with moderately good performance, although at varying degree of effectiveness and under different conditions. |
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution (2025.emnlp-main)
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| Challenge: | Open Large Language Models (OLLMs) are increasingly leveraged in generative AI applications, posing new challenges for detecting their outputs. |
| Approach: | They propose a benchmark to train and evaluate machine-generated text detectors on Turing Test and Authorship Attribution problems. |
| Outcome: | The proposed detector outperforms existing detectors in varying degrees of difficulty and relevance across tasks. |
Argument Component Segmentation with Fine-Tuned Large Language Models (2026.findings-eacl)
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| Challenge: | Argument Mining (AM) aims to identify and interpret argumentative structures in unstructured text. |
| Approach: | They propose a fine-grained, paired-tag annotation schema that distinguishes between relevant and surrounding content. |
| Outcome: | The proposed approach performs comparable to human expert annotators across multiple benchmark datasets. |
Authorship Attribution in Multilingual Machine-Generated Texts (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have reached human-like fluency and coherence, but distinguishing machine-generated text from human-written content becomes increasingly difficult. |
| Approach: | They propose a problem of multilingual authorship attribution (AA) that involves attributing texts to human or multiple LLM generators across diverse languages. |
| Outcome: | The proposed method can be adapted to multilingual settings, but still has significant limitations and challenges. |
Exploring LLMs’ Ability to Spontaneously and Conditionally Modify Moral Expressions through Text Manipulation (2025.acl-long)
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| Challenge: | Existing studies on moral-related tasks based on large language models have not been conducted. |
| Approach: | They analyze behavior of Large Language Models (LLMs) among open and uncensored models and use human-annotated datasets to analyze moral-related data. |
| Outcome: | The results show that large language models can alter moral dimensions through text manipulation tasks and moral-related conditioning prompts. |