arXiv NLP research summaries for May 21, 2024.

Today's Research Themes (AI-Generated):

• A new method is proposed for the scalable and precise identification of crucial 'circuits' within large language models using sparse autoencoders.

• SirLLM enhances Large Language Models (LLMs) with the ability to maintain extended memory for infinite-length dialogues without fine-tuning.

• Pyramid KV cache compression is introduced to significantly increase the throughput and decrease memory usage in LLM inference.

• ProtT3, a Protein-to-Text Generation framework, is developed to aid Language Models in understanding and generating information from amino acid sequences.

• Self-instruction based fine-tuning is shown to balance fact-checking accuracy and explainability in LLMs, while ensuring data security.