RAM: Towards an Ever-Improving Memory System by Learning from Communications
Authors: Jiaqi Li, Xiaobo Wang, Zihao Wang, Zilong Zheng


Reviewer paper summaries: This paper presents RAM (Retrieval Augmented Memory), a framework designed to enable LLMs to continually learn and update their knowledge base through human feedback and recursive reasoning-based retrieval. Inspired by human pedagogical processes, RAM employs a dynamic memory system that enhances LLMs’ knowledge and reasoning capabilities without requiring extensive retraining. The framework is particularly effective in handling complex tasks such as false premise and multi-hop questions. Extensive experiments with both simulated and real users show significant improvements in performance over traditional methods.
Summary of strengths: - The introduction of a dynamic, ever-improving memory system for LLMs is a novel contribution, addressing the limitations of static knowledge representations in current models.
- The framework’s ability to continually update knowledge based on human feedback makes it highly practical for real-world applications where information is constantly evolving.
Summary of weaknesses: - The effectiveness of RAM heavily relies on the quality and accuracy of human feedback, which can be variable and introduce potential biases.
- The paper could benefit from a more detailed analysis of failure cases to better understand the limitations and areas for improvement in the RAM framework.
- Simplifying the implementation complexity of RAM would make it more accessible for practical applications, potentially through more streamlined algorithms or automated processes.
- The scalability of RAM and the mechanisms for obtaining feedback during inference need further exploration.
- The section on Knowledge From Human Feedback lacks clarity. The strategies, cost, and frequency of human feedback, as well as the proportion of the three categories, should be more clearly defined.
- The use of different types of models as backbones, such as Mistral or Qwen, should be considered and discussed.
- More implementation details about the GPT4_score should be provided, along with a more thorough explanation of the human evaluation process.
- An analysis of the causes of errors, particularly whether they stem from model hallucinations or other issues, would be beneficial.
- The impact of memory size and capacity on speed as the memory grows should be addressed.
Comments, suggestions and typos: - The paper appears to use the wrong template. The margins are too wide and do not match the ACL default template.
Overall assessment scores: 2