Large Language Model
Supplementary Material Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition Croissant metadata link 1
The Croissant Akhtar et al. (2024) metadata can be found at We present the data card, following the format proposed by Pushkarna et al. (2022). "By using this chat interface and the API, you accept that the interactions with the interface and the API can be used for research purposes, and potentially open-sourced by the competition organizers." The dataset is divided into two splits: defenses and chats . For more details, see Section 4 in the main paper. See Figure 1 for an example.
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs).
Interpreting Learned Feedback Patterns in Large Language Models Luke Marks Amir Abdullah Clement Neo Rauno Arike David Krueger Philip T orr
Learned Feedback Pattern (LFP) for patterns in an LLM's activations learned during RLHF that improve its performance on the fine-tuning task.
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity
Biodiversity plays a multifaceted role in sustaining ecosystems and supporting human well-being. Primarily, it serves as a cornerstone for ecosystem stability and resilience, providing a natural defence against disturbances such as climate change and invasive species (Cardinale et al., 2012).
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
These components enhance the model's ability to extract
