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As LLMs have become capable of processing more complex types of inputs,researchers have recently studied how to efficiently and affordably processpossibly arbitrarily long sequences. One effective approach is to use a FIFOmemory to store keys and values of an attention sublayer from past chunks toallow subsequent queries to attend. However, this approach requires a largememory and/or takes into the consideration the specific LM architecture.Moreover, due to the causal nature between the key-values in prior context andthe queries at present, this approach cannot be extended to bidirectionalattention such as in an encoder-decoder or PrefixLM decoder-only architecture.In this paper, we propose to use eviction policies, such as LRA and LFA, toreduce the memory size and adapt to various architectures, and we also proposethe Attendre layer, a wait-to-attend mechanism by retrieving the key-valuememory (K/V memory) with evicted queries in the query memory (Q memory). As afirst step, we evaluate this method in the context length extension setup usingthe TriviaQA reading comprehension task, and show the effectiveness of theapproach.

Attendre: Wait To Attend By Retrieval With Evicted Queries in Memory-Based Transformers for Long Context Processing