While current large language models (LLMs) demonstrate some capabilities in knowledge-intensive tasks, they are limited by relying on their parameters as an implicit storage mechanism. As a result, they struggle with infrequent knowledge and temporal degradation. In addition, the uninterpretable nature of parametric memorization makes it challenging to understand and prevent hallucination. Parametric memory pools and model editing are only partial solutions. Retrieval Augmented Generation (RAG) $\unicode{x2013}$ though non-parametric $\unicode{x2013}$ has its own limitations: it lacks structure, complicates interpretability and makes it hard to effectively manage stored knowledge. In this paper, we introduce MemLLM, a novel method of enhancing LLMs by integrating a structured and explicit read-and-write memory module. MemLLM tackles the aforementioned challenges by enabling dynamic interaction with the memory and improving the LLM's capabilities in using stored knowledge. Our experiments indicate that MemLLM enhances the LLM's performance and interpretability, in language modeling in general and knowledge-intensive tasks in particular. We see MemLLM as an important step towards making LLMs more grounded and factual through memory augmentation.

Chowbotics is packing up Sally the salad making robot and sending it off to college. Well, many colleges actually, as the food robotics startup is set to announce next week a bigger push into the higher education market. Chowbotics told us that this school year, students at multiple colleges and universities in the U.S. will be able to buy salads and breakfast bowls from Sally the robot. Those schools include: Case Western Reserve University in Cleveland, OH; College of the Holy Cross in Worcester, MA; the University of Guelph in Ontario, Canada; Elmira College in Elmira, NY; the University of Memphis in Memphis, TN; and Wichita State University in Wichita, KS. These schools join Marshall University in Huntington, WV, which installed Sally in 2018.