Papers with open-source solutions
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. |
Truth, Trust, and Trouble: Medical AI on the Edge (2025.emnlp-industry)
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Mohammad Anas Azeez, Rafiq Ali, Ebad Shabbir, Zohaib Hasan Siddiqui, Gautam Siddharth Kashyap, Jiechao Gao, Usman Naseem
| Challenge: | Large Language Models (LLMs) are promising for transforming digital health applications . but ensuring they meet industry standards for factual accuracy, usefulness, and safety remains a challenge . |
| Approach: | They present a framework to assess large language models' accuracy, usefulness, and safety . they assess models' honesty, helpfulness, harmlessness and domain-specific tuning . |
| Outcome: | The proposed framework assesses models across honesty, helpfulness, and harmlessness . AlpaCare-13B achieves highest accuracy (91.7%) and harmlessity (0.92) . |