title: Self-RAG: Learning to Retrieve, Generate, and Critique through
    Self-Reflection
summary: 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.
paper_authors._id: 6530af4370cebad89be236fb
paper_authors.name: Akari Asai
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-10-19T08:10:15.751Z
paper_authors._id: 6266e0cb7a1f5a1562c4e86e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6266e0cb7a1f5a1562c4e86e/kkJZPssa76mdRPnKH-q3e.jpeg
paper_authors.fullname: Akari Asai
paper_authors.type: user
paper_authors.user: akariasai
paper_authors._id: 6530af4370cebad89be236fc
paper_authors.name: Zeqiu Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-19T08:10:39.980Z
paper_authors._id: 647d335836e109abce3ffb79
paper_authors.avatarUrl: /avatars/fee73f1e15502ca51f8a7a928f653d31.svg
paper_authors.fullname: Zeqiu Wu
paper_authors.type: user
paper_authors.user: zeqiuwu1
paper_authors._id: 6530af4370cebad89be236fd
paper_authors.name: Yizhong Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-19T08:10:47.390Z
paper_authors._id: 6269be67d1ac0cde592aba29
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6269be67d1ac0cde592aba29/EP3zZbD7-jWK9ITvHGbnZ.jpeg
paper_authors.fullname: Yizhong Wang
paper_authors.type: user
paper_authors.user: yizhongw
paper_authors._id: 6530af4370cebad89be236fe
paper_authors.name: Avirup Sil
paper_authors._id: 6530af4370cebad89be236ff
paper_authors.name: Hannaneh Hajishirzi
paper_title: Self-RAG: Learning to Retrieve, Generate, and Critique through
    Self-Reflection
paper_summary: 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.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) Self-Reflective Retrieval-Augmented Generation (Self-RAG) parametric knowledge reflection tokens inference phase adaptively retrieves passages on-demand passage retrieval fact verification tasks long-form generations ChatGPT Llama2-chat OpenAI Codex GPT-4 T5 Blenderbot DialoGPT OpenAI Codex GPT-4 T5 Blenderbot DialoGPT