The field of large language models (LLMs) is rapidly advancing, with a focus on improving their ability to generate high-quality text while mitigating unwanted behaviors such as memorization and prior-dominated responses. Recent work has demonstrated the potential for LLMs to extract entire books from their training data, highlighting the need for more effective regurgitation mitigation strategies. Furthermore, researchers have made progress in identifying and mitigating the influence of prior distributions in LLMs, which can lead to incorrect responses in deterministic tasks. Additionally, new architectures and techniques, such as sequential Monte Carlo, have been developed to impose syntactic and semantic constraints on LLM generation, enabling more controlled and flexible text generation. Notable papers in this area include:

• One study that demonstrated the ability to extract entire books from LLMs using prefix-prompting techniques.
• Research that showed how to identify and manipulate the prior distribution in LLMs to improve performance on prior-dominated tasks.
• A paper that introduced an SMC framework for controlled LLM generation, allowing small models to outperform larger ones.