Model-tuning Via Prompts Makes NLP Models Adversarially Robust Paper • 2303.07320 • Published Mar 13, 2023
Scaling Laws for Data Filtering -- Data Curation cannot be Compute Agnostic Paper • 2404.07177 • Published Apr 10, 2024
Rethinking LLM Memorization through the Lens of Adversarial Compression Paper • 2404.15146 • Published Apr 23, 2024
OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics Paper • 2506.12618 • Published Jun 14
BeyondWeb: Lessons from Scaling Synthetic Data for Trillion-scale Pretraining Paper • 2508.10975 • Published 10 days ago • 53
locuslab/mix_ift_v9-smollm2-1.7b-score0_rephrase123_mild_ref45_metadata45_10p-600B-metamix3p-1k-0 2B • Updated May 6 • 6
locuslab/mix_ift_v9-smollm2-1.7b-score0_rephrase123_mild_ref45_metadata45_10p-600B-metamix3p-1k-0 2B • Updated May 6 • 6
locuslab/mix_ift_v9-smollm2-1.7b-score0_rephrase123_mild_ref45_metadata_5p-600B-metamix3p-1k-0 2B • Updated May 6 • 33
locuslab/mix_ift_v9-smollm2-1.7b-score0_rephrase123_mild_ref45_metadata_5p-600B-metamix3p-1k-0 2B • Updated May 6 • 33
locuslab/mix_ift_v9-smollm2-1.7b-score0_rephrased_from_beginning_meta-600B-metamix3p-1k-0 2B • Updated May 6 • 6
locuslab/mix_ift_v9-smollm2-1.7b-score0_rephrased_from_beginning_meta-600B-metamix3p-1k-0 2B • Updated May 6 • 6