Large Language Models (LLMs) have taken the world by a storm. They are incresingly used for various applications, such as chatbots, summarization, and question-answering. However, one of the major limitations of Large Language Models (LLMs) is their difficulty in dealing with multi-step reasoning problems due to their limited memory capacity. This is where the paper, "Learning to Reason and Memorize with Self Notes" comes in, proposing a mechanism to equip LLMs with a scratchpad-like feature that is embedded in the context to guide them in solving complex problems that require memory.
The paper introduces the concept of "Self Notes" - a way to break down the context into multiple steps and perform intermediate reasoning, allowing the model to collect and distill information several times before making the final output decision. Self Notes are an interesting method that equips LLMs with a way to store extra intermediate memory as part of the context, which is useful for tasks that require multi-step reasoning or memory. Self Notes work better than alternative methods, but there is a limitation in that you need to fine-tune on a dataset that has been annotated with a suitable format.
The authors of the paper performed experiments using the GPT-2 model, comparing the vanilla approach to the scratchpad and Self Notes approach, on various reasoning tasks. The results showed that Self Notes is much better for solving complex problems that require memory. However, the best model had to be trained on datasets that were annotated with the Self Notes format, which is not ideal if you want to use an LLM out of the box. The authors proposed a method for unsupervised fine-tuning using the Self Notes format to help in overcoming this limitation.
The paper highlights a vital problem with LLMs - their long-term memory. Even short-term memory can be an issue, and this is something that the NLP community needs to address moving forward. The authors propose a relatively simple mechanism that allows LLMs to store and retrieve valuable information and memories, helping to inform future decisions.
In conclusion, the paper "Learning to Reason and Memorize with Self Notes" is an interesting contribution to the NLP field. It introduces an innovative approach to equip LLMs with a way to store extra intermediate memory as part of the context, making them better at solving complex problems that require memory. While there are some limitations, Self Notes have the potential to significantly enhance the capabilities of LLMs and open up new avenues for research in NLP.