SHIELD: Evaluation and Defense Strategies for Copyright Compliance in LLM Text Generation (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have transformed machine learning but have raised significant legal concerns due to their potential to produce text that infringes on copyrights. |
| Approach: | They propose a lightweight, real-time defense mechanism to prevent the generation of copyrighted text by evaluating methods and testing attack strategies. |
| Outcome: | The proposed defense significantly reduces the volume of copyrighted text generated by LLMs by effectively refusing malicious requests. |
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| Challenge: | a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs . |
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| Challenge: | Recent work on Large Language Models (LLMs) has identified a number of approaches to protect against their vulnerabilities and safety. |
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From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance across various tasks, effectively following instructions to meet diverse user needs. |
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