Improving Factuality with Explicit Working Memory

In the realm of long-form text generation, a notable vulnerability of large language models (LLMs) is their propensity for hallucination, wherein the generated text contains factually inaccurate information. By prepending the input prompt with relevant documents from trustworthy sources, retrieved-augmented generation (RAG) (Lewis et al., 2020; Shi et al., 2024) has been shown to be a simple yet effective approach that substantially mitigates the hallucination issue. To further enhance the factual accuracy of model output, various iterative prompting methods have been proposed that build upon RAG. For instance, FLARE (Jiang et al., 2023) generates responses sentence by sentence, and if a newly generated sentence contains low-probability tokens, it retrieves a new set of documents and re-runs RAG to regenerate the sentence. Alternatively, Self-RAG (Asai et al., 2024) employs a self-critic component to verify the correctness of each partial generation and repeatedly queries a retrieval system to update the background knowledge, thereby producing more accurate and faithful responses. While these systems demonstrate significant empirical improvement, they are restricted in the traditional RAG design. Context-relevant knowledge through retrieval is the only online feedback to the model, incorporated as part of the input string.