BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?

Language models have seen significant growth in the size of their corpus, leading to notable performance improvements. Yet, there has been limited progress in developing models that handle smaller, more human-like datasets. As part of the BabyLM shared task, this study explores the impact of reinforcement learning from human feedback (RLHF) on language models pretrained from scratch with a limited training corpus. Comparing two GPT-2 variants, the larger model performs better in storytelling tasks after RLHF fine-tuning. These findings suggest that RLHF techniques may be more advantageous for larger models due to their higher learning and adaptation capacity, though more experiments are needed to confirm this finding. These insights highlight the potential benefits of RLHF fine-tuning for language models within limited data, enhancing their ability to maintain narrative focus and coherence while adhering better to initial instructions in storytelling tasks. The code for this work is publicly at https://github.com/Zephyr1022/BabyStories-UTSA.Comment: Accepted to BabyLM workshop at CoNL

CRISPR/Cas9-based Genome Editing in Pseudomonas aeruginosa and Cytidine Deaminase-Mediated Base Editing in Pseudomonas Species

Summary: Pseudomonas species are a large class of gram-negative bacteria that exhibit significant biomedical, ecological, and industrial importance. Despite the extensive research and wide applications, genetic manipulation in Pseudomonas species, in particular in the major human pathogen Pseudomonas aeruginosa, remains a laborious endeavor. Here we report the development of a genome editing method pCasPA/pACRISPR by harnessing the CRISPR/Cas9 and the phage λ-Red recombination systems. The method allows for efficient and scarless genetic manipulation in P. aeruginosa. By engineering the fusion of the cytidine deaminase APOBEC1 and the Cas9 nickase, we further develop a base editing system pnCasPA-BEC, which enables highly efficient gene inactivation and point mutations in a variety of Pseudomonas species, such as P. aeruginosa, Pseudomonas putida, Pseudomonas fluorescens, and Pseudomonas syringae. Application of the two genome editing methods will dramatically accelerate a wide variety of investigations, such as bacterial physiology study, drug target exploration, and metabolic engineering. : Genetics; Microbial Genetics; Biotechnology; Genetic Engineering Subject Areas: Genetics, Microbial Genetics, Biotechnology, Genetic Engineerin