Recitation-Augmented Language Models

Zhiqing Sun, Xuezhi Wang, Yi Tay, Yiming Yang, Denny Zhou

Keywords: Large Language Models, In-context Learning, Memorization, Closed-book Question Answering, CBQA

Abstract: We propose a new paradigm to help Large Language Models (LLMs) generate more accurate factual knowledge without retrieving from an external corpus, called RECITation-augmented gEneration (RECITE). Different from retrieval-augmented language models that retrieve relevant documents before generating the outputs, given an input, RECITE first recites one or several relevant passages from LLMs’ own memory via sampling, and then produces the final answers. We show that RECITE is a powerful paradigm for knowledge-intensive NLP tasks. Specifically, we show that by utilizing recitation as the intermediate step, a recite-and-answer scheme can achieve new state-of-the-art performance in various closed-book question answering (CBQA) tasks. In experiments, we verify the effectiveness of RECITE on three pre-trained models (In-house LM, UL2, and OPT) and three CBQA tasks (Natural Questions, TriviaQA, and HotpotQA). Our code is available at "https://github.com/Edward-Sun/RECITE".

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TL;DR: We propose a novel recitation-augmented generation framework to improve language models’ performance in the closed-book question-answering setting.

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/arxiv:2210.01296/code)