Pretrained 1B/8B param models, with controlled insertion of texts designed to emulate key memorization risks: copyright (e.g., book passages), privacy (e.g., synthetic biographies), and test set contamination
Pretrained 1B/8B param models, with controlled insertion of texts designed to emulate key memorization risks: copyright (e.g., book passages), privacy (e.g., synthetic biographies), and test set contamination
Pretrained 1B/8B param models, with controlled insertion of texts designed to emulate key memorization risks: copyright (e.g., book passages), privacy (e.g., synthetic biographies), and test set contamination
@pietrolesci.bsky.social who did a fantastic job!
#ACL2025
@pietrolesci.bsky.social who did a fantastic job!
#ACL2025
Would love to connect and chat about LLM planning, reasoning, AI4Science, multimodal stuff, or anything else. Feel free to DM!
Would love to connect and chat about LLM planning, reasoning, AI4Science, multimodal stuff, or anything else. Feel free to DM!
Would love to connect and chat about LLM planning, reasoning, AI4Science, multimodal stuff, or anything else. Feel free to DM!
Introducing TLDR: Token-Level Detective Reward Model For Large Vision Language Models.
TLDR provides fine-grained annotations to
each text token.
🔗arXiv: arxiv.org/abs/2410.04734
Introducing TLDR: Token-Level Detective Reward Model For Large Vision Language Models.
TLDR provides fine-grained annotations to
each text token.
🔗arXiv: arxiv.org/abs/2410.04734
@aclmeeting.bsky.social in Vienna 🎉
💡 L2M2 brings together researchers to explore memorization from multiple angles. Whether it's text-only LLMs or Vision-language models, we want to hear from you! 🌍