LM2: Large Memory Models
This work addresses the limitations of standard Transformers for users who require enhanced multi-step reasoning and relational argumentation capabilities, particularly in applications involving long contexts and complex information synthesis.
The Large Memory Model (LM2) tackles the limitations of standard Transformers in multi-step reasoning and achieves a 37.1% and 86.3% improvement over the RMT and Llama-3.2 models on the BABILong benchmark, respectively. It also exhibits a 5.0% improvement over a pre-trained vanilla model on the MMLU dataset.
This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standard Transformers in multi-step reasoning, relational argumentation, and synthesizing information distributed over long contexts. The proposed LM2 incorporates a memory module that acts as a contextual representation repository, interacting with input tokens via cross attention and updating through gating mechanisms. To preserve the Transformers general-purpose capabilities, LM2 maintains the original information flow while integrating a complementary memory pathway. Experimental results on the BABILong benchmark demonstrate that the LM2model outperforms both the memory-augmented RMT model by 37.1% and the baseline Llama-3.2 model by 86.3% on average across tasks. LM2 exhibits exceptional capabilities in multi-hop inference, numerical reasoning, and large-context question-answering. On the MMLU dataset, it achieves a 5.0% improvement over a pre-trained vanilla model, demonstrating that its memory module does not degrade performance on general tasks. Further, in our analysis, we explore the memory interpretability, effectiveness of memory modules, and test-time behavior. Our findings emphasize the importance of explicit memory in enhancing Transformer architectures.