Going beyond RAG: Extended Mind Transformers - Phoebe Klett
Sep 11, 2024 · 16:04
Phoebe Klett presents Extended Mind Transformers (EMT), a modification to transformer attention that lets models retrieve relevant memory tokens during generation without fine-tuning. EMT uses relative position embeddings (RoPE in LLaMA, ALiBi in MPT) to enable zero-shot generalization. On a counterfactual retrieval benchmark up to 16K tokens, EMT outperforms fine-tuned models and, combined with RAG, surpasses GPT-4. It provides granular citations by showing exactly which memory tokens were attended to, and reduces hallucinations via active learning: when token-level entropy signals uncertainty, the model retrieves more memories. EMT is open-sourced on Hugging Face and GitHub with configurable parameters like stride length, top K, similarity masking, and unknown token elimination.
