Just read twice: closing the recall gap for recurrent language models

Recurrent large language models that compete with Transformers in language modeling perplexity are emerging at a rapid rate (e.g., Mamba, RWKV). Excitingly, these architectures use a constant amount of memory during inference. However, due to the limited memory, recurrent LMs cannot recall and use all the information in long contexts leading to brittle in-context learning (ICL) quality. A key challenge for efficient LMs is selecting what information to store versus discard. In this work, we observe the order in which information is shown to the LM impacts the selection difficulty. To formalize this, we show that the hardness of information recall reduces to the hardness of a problem called set disjointness (SD), a quintessential problem in communication complexity that requires a streaming algorithm (e.g., recurrent model) to decide whether inputted sets are disjoint. We empirically and theoretically show that the recurrent memory required to solve SD changes with set order, i.e., whether the smaller set appears first in-context. Our analysis suggests, to mitigate the reliance on data order, we can put information in the right order in-context or process prompts non-causally. Towards that end, we propose: (1) JRT-Prompt, where context gets repeated multiple times in the prompt, effectively showing the model all data orders. This gives \(11.0 \pm 1.3\) points of improvement, averaged across \(16\) recurrent LMs and the \(6\) ICL tasks, with \(11.9\times\) higher throughput than FlashAttention-2 for generation prefill (length \(32\)k, batch size \(16\), NVidia H100). We then propose (2) JRT-RNN, which uses non-causal prefix-linear-attention to process prompts and provides \(99\%\) of Transformer quality at \(360\)M params., \(30\)B tokens and \(96\%\) at \(1.3\)B params., \(50\)B tokens on average across the tasks, with \(19.2\times\) higher throughput for prefill than FA2.