A Long-term Memory for ChatGPT

Generated by Gemini

In October last year, I published a short position paper, A Case-Based Persistent Memory for a Large Language Model, arguing that ChatGPT and other LLMs need a persistent long-term memory of their interactions with a user to be truly useful. It seems OpenAI was listening because a couple of days ago, they announced that ChatGPT would retain a persistent memory of chats across multiple conversations. As reported in Wired, the memory will be used to add helpful background context to your prompts, improving their specificity to you over time. I argued in my October paper that the LLM community should look to the Case-Based Reasoning community for help with memory since we are the discipline within AI that has been explicitly concerned with memory for decades. For example, we long ago realised that while remembering is vital, a memory must also be able to forget some things to remain functional. This is a non-trivial problem discussed in Smyth and Keane's 1997 paper Remembering To Forget: A Competence-Preserving Case Deletion Policy for Case-Based Reasoning Systems. The synergies between CBR and LLMs will be the focus of a workshop at ICCBR-24 in July in Merida, Yucatán, México.

The Foundation Model (LLM) Transparency Index

A new index compiled by the Stanford University Center for Research on Foundation Models (CRFM) rates the transparency of 10 foundation model companies and finds them lacking. The best, Meta’s Llama 2, only scores 54% across 100 different aspects of transparency. As LLMs become more widespread and embedded into our lives, their transparency includes the computational resources, data, and labour used to build foundation models, the specifics of their architectures and their downstream use. You can read about The Foundation Model Transparency Index here. #LLM