A novel approach extends memory and enables multi-step reasoning in large language models
- Self-Notes method addresses limitations in context memory and multi-step reasoning in large language models.
- Unlike scratchpad approaches, Self-Notes allows models to deviate from input context for reasoning and recall.
- Experiments demonstrate that Self-Notes improves performance and generalization in longer sequences.
- Reducing the amount of supervision during training is a potential future research direction.
- Self-Notes method shows promise in both synthetic and real-world tasks using the GPT-2 base model.
Large language models often struggle with limited context memory and multi-step reasoning. A proposed method, known as Self-Notes, aims to solve these problems by allowing the model to take notes as it processes input context. This approach differs from recent scratchpad methods, as the model can deviate from the input context at any time, enabling reasoning and recall on the fly. As a result, Self-Notes extends memory capabilities and facilitates multi-step reasoning.
One advantage of interleaving reasoning with context in this manner is that reasoning steps can be closer to their relevant context. Additionally, Self-Notes can act as a recurrent memory, as the answers generated by the model are fed back into it. These advantages contribute to the method’s ability to scale better with longer sequences, as demonstrated in various experiments.
Furthermore, the experiments show that reducing the amount of Self-Note supervision during training does not result in a significant performance drop. Future research could explore using reinforcement learning to discover optimal Self-Notes and investigate whether larger models can generate effective Self-Note questions without supervision. Another potential direction is to combine Self-Notes with scratchpad methods, which could enhance backward reasoning capabilities.
Self-Notes has been tested on the 124M parameter GPT-2 base model across five different synthetic and real-world tasks, demonstrating its promise in improving language model performance. Training larger models with Self-Notes remains a challenge to be addressed in future work.