Disclaimer: This podcast is completely AI generated by ⁠⁠⁠⁠⁠⁠NoteBookLM⁠⁠⁠⁠⁠⁠ 🤖 Summary In this episode we discuss about an article of IBM Research scientists, who presented research at the ACL conference on improving large language models (LLMs). Two key approaches were explored: deductive closure training, where LLMs evaluate their own output for consistency and accuracy, improving generation accuracy by up to 26%; and self-specialisation, which efficiently transforms generalist LLMs into subject-matter experts using minimal labelled data, significantly boosting performance in fields like finance and biomedicine. These methods aim to enhance LLM accuracy and efficiency, addressing limitations of existing techniques. The results demonstrate the potential for LLMs to improve themselves, reducing the need for extensive human intervention and computational resources.