Papers by Giulio Zizzo
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models (2023.acl-long)
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| Challenge: | generative large language models (LLMs) are widely used but fine-tuned to improve performance on downstream applications leads to violations of model licenses, model theft, and copyright infringement. |
| Approach: | They propose to trace back the origin of a model trained to its pre-trained base model . they use different knowledge levels and attribution strategies to find out how the model was trained . |
| Outcome: | The proposed method can trace back 8 out of 10 fine tuned models with different knowledge levels and attribution strategies. |
Granite Guardian: Comprehensive LLM Safeguarding (2025.naacl-industry)
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Inkit Padhi, Manish Nagireddy, Giandomenico Cornacchia, Subhajit Chaudhury, Tejaswini Pedapati, Pierre Dognin, Keerthiram Murugesan, Erik Miehling, Martín Santillán Cooper, Kieran Fraser, Giulio Zizzo, Muhammad Zaid Hameed, Mark Purcell, Michael Desmond, Qian Pan, Inge Vejsbjerg, Elizabeth M. Daly, Michael Hind, Werner Geyer, Ambrish Rawat, Kush R. Varshney, Prasanna Sattigeri
| Challenge: | a suite of advanced models is designed to detect and mitigate risks associated with prompts and responses. |
| Approach: | a team of researchers develop a model family to detect and mitigate risks associated with prompts and responses. the model family is based on the Granite 3.0 language models. |
| Outcome: | a new model family is designed to detect and mitigate risks associated with prompts and responses. |