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With direct access to human-written reference as memory, retrieval-augmentedgeneration has achieved much progress in a wide range of text generation tasks.Since better memory would typically prompt better generation~(we define this asprimal problem). The traditional approach for memory retrieval involvesselecting memory that exhibits the highest similarity to the input. However,this method is constrained by the quality of the fixed corpus from which memoryis retrieved. In this paper, by exploring the duality of the primal problem:better generation also prompts better memory, we propose a novel framework,selfmem, which addresses this limitation by iteratively employing aretrieval-augmented generator to create an unbounded memory pool and using amemory selector to choose one output as memory for the subsequent generationround. This enables the model to leverage its own output, referred to asself-memory, for improved generation. We evaluate the effectiveness of selfmemon three distinct text generation tasks: neural machine translation,abstractive text summarization, and dialogue generation, under two generationparadigms: fine-tuned small model and few-shot LLM. Our approach achievesstate-of-the-art results in four directions in JRC-Acquis, XSum (50.3 ROUGE-1),and BigPatent (62.9 ROUGE-1), demonstrating the potential of self-memory inenhancing retrieval-augmented generation models. Furthermore, we conductthorough analyses of each component in the selfmem framework to identifybottlenecks and provide insights for future research.

Lift Yourself Up: Retrieval-augmented Text Generation with Self Memory