NeurIPS Rebuttal for "Retrieval-Augmented Generation for Knowledge-Intensive NLP T asks" We thank reviewers for their thoughtful, detailed reviews. "information retrieval strategy to improve the the generation Pre-trained seq2seq models have only become available in the last year (T5, BART) or two (GPT2). We study two RAG models. RAG-Sequence's formulation is similar to REALM, but RAG-Token is novel and Further, we explore novel decoding strategies for these models. "contribution [...] is not very specific, since R1 suggested that "A figure or example about P AG-Sequence Model and P AG-Token Model is needed", and R3 mentions "description of the model is quite concise (due to space restrictions)".

Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).

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In this regard, we propose a novel memory mechanism that represents past events in human language.

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Traditional approaches focus on aligning models during the instruction tuning orreinforcement learning stages, referred tointhis paperas'postalignment'.