Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
akhaliq
Abstract
Despite their remarkable capabilities, large language models (LLMs) often produce responses containing factual inaccuracies due to their sole reliance on the parametric knowledge they encapsulate. Retrieval-Augmented Generation (RAG), an ad hoc approach that augments LMs with retrieval of relevant knowledge, decreases such issues. However, indiscriminately retrieving and incorporating a fixed number of retrieved passages, regardless of whether retrieval is necessary, or passages are relevant, diminishes LM versatility or can lead to unhelpful response generation. We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) that enhances an LM's quality and factuality through retrieval and self-reflection. Our framework trains a single arbitrary LM that adaptively retrieves passages on-demand, and generates and reflects on retrieved passages and its own generations using special tokens, called reflection tokens. Generating reflection tokens makes the LM controllable during the inference phase, enabling it to tailor its behavior to diverse task requirements. Experiments show that Self-RAG (7B and 13B parameters) significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks. Specifically, Self-RAG outperforms ChatGPT and retrieval-augmented Llama2-chat on Open-domain QA, reasoning and fact verification tasks, and it shows significant gains in improving factuality and citation accuracy for long-form generations relative to these models.
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LLMs make a ton of factual mistakes. This limits using them for real-world stuff where being right matters.
Some researchers tried "retrieval augmentation" before - prepending context from Wikipedia to give the LM knowledge. But this is slow and doesn't guarantee the output will actually use the facts correctly.
Researchers from UW and IBM take a clever and different approach. They train the LM to "reflect" on itself - critiquing when it needs more info and whether its output matches the evidence.
The model learns to generate special "retrieve" tokens to selectively get relevant facts from Wikipedia only when needed. It also generates "critique tokens" to check if its output is properly supported.
They tested this "SELF-RAG" framework on question answering, reasoning, and long-form generation tasks. It beat normal LLMs and retrieval-augmented ones.
The key findings:
- SELF-RAG models (7B and 13B parameters) won on 6 diverse tasks against strong baselines.
- It had 81% accuracy on a fact checking task, way better than 71% for another new technique.
- For biography generation, it scored 80% on factuality versus just 71% for ChatGPT.
- It achieved much higher citation accuracy - properly attributing claims to evidence sources.
This shows the benefits of having LLMs reflect on their own limitations and selectively get knowledge.
There are still issues though - SELF-RAG can still make unsupported claims sometimes. More work is needed on training, knowledge sources, and self-critique reliability.
But overall it's an elegant approach to improving factuality without sacrificing too much of the creative versatility of large models. Really keen to see how this research direction develops!
TLDR: Researchers made LMs learn to notice their own mistakes and retrieve knowledge to correct themselves. Early results show improvements in factual accuracy.
这个论文讲了什么
摘要
Self-RAG（Self-Reflective Retrieval-Augmented Generation）是一个新提出的框架，旨在通过检索和自我反思增强大型语言模型（LLMs）的输出质量和事实准确性。该框架训练了一个单一的、任意的大型语言模型，使其能够按需自适应地检索相关的文本段落，并通过特殊的 “反思令牌”（reflection tokens）来生成和反思检索到的文本和自身的生成内容。这种方法使得在推理阶段，模型能够根据不同任务的要求进行控制，从而提高了其在开放域问答、推理、事实验证任务以及长篇生成中的事实性和引用准确性。实验结果表明，Self-RAG（具有 70 亿和 130 亿参数的模型）在多样化的任务集上显著超越了现有的大型语言模型和检索增强模型，如 ChatGPT 和 Llama2-chat。具体来说，Self-RAG 在 Open-domain QA、 reasoning 和 fact verification 任务上的表现优于这些模型，并且在长篇生成的事实性和引用准确性方面显示出了显著的改进。
观点
大型语言模型（LLMs）虽然具有显著的能力，但由于依赖其内部的参数知识，往往会产生事实不准确的回答。Self-RAG 框架通过增加检索和自我反思的机制，有效地减少了这种问题。
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Self-RAG 框架使得大型语言模型能够自适应地按需检索相关信息，并通过生成特殊的反思令牌来评估和改进其生成的内容，这使得模型在推理阶段具有可控性，能够适应多样化的任务需求。
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实验结果显示，与现有的大型语言模型和检索增强模型相比，Self-RAG（7B 和 13B 参数版本）在多种任务上表现出色，尤其是在提高事实性和引用准确性方面。
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Self-RAG 模型在事实检查任务上达到了 81% 的准确率，相比之下，另一种新技术只有 71% 的准确率。在生物图传生成任务中，Self-RAG 的事实性得分为 80%，而 ChatGPT 只有 71%。
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尽管 Self-RAG 取得了显著的进步，但仍然存在生成不受支持的声明的问题，这意味着需要对训练过程、知识源和自我批判的可靠性进行进一步的研究和改进。
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Self-RAG 的方法被认为是提高大型模型的事实准确性而不牺牲其创造性多样性的一种优雅的方法。
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社区成员对 Self-RAG 的研究方向表示积极的期待，并认为这种能够让模型自我识别错误并选择性地获取知识以进行纠正的方法，对于提高事实准确性具有重要意义。
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Boosting AI Accuracy: Unveiling Self-RAG for Reliable Responses
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