Summary of Understanding Finetuning For Factual Knowledge Extraction, by Gaurav Ghosal et al.
Understanding Finetuning for Factual Knowledge Extractionby Gaurav Ghosal, Tatsunori Hashimoto, Aditi RaghunathanFirst submitted to arxiv…
Understanding Finetuning for Factual Knowledge Extractionby Gaurav Ghosal, Tatsunori Hashimoto, Aditi RaghunathanFirst submitted to arxiv…
Exploring Design Choices for Building Language-Specific LLMsby Atula Tejaswi, Nilesh Gupta, Eunsol ChoiFirst submitted to…
Information Guided Regularization for Fine-tuning Language Modelsby Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin…
PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomesby He Cao, Yanjun Shao, Zhiyuan Liu, Zijing Liu,…
BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversityby Zahra Gharaee, Scott C. Lowe, ZeMing Gong, Pablo…
Learning sum of diverse features: computational hardness and efficient gradient-based training for ridge combinationsby Kazusato…
DataComp-LM: In search of the next generation of training sets for language modelsby Jeffrey Li,…
Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuningby Tian Liu, Huixin Zhang, Shubham Parashar, Shu KongFirst submitted…
Probing the Decision Boundaries of In-context Learning in Large Language Modelsby Siyan Zhao, Tung Nguyen,…
Data Shapley in One Training Runby Jiachen T. Wang, Prateek Mittal, Dawn Song, Ruoxi JiaFirst…