Self-RAG: Learning to Retrieve, Generate, and Critique through
  Self-Reflection
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.
6530af4370cebad89be236fb
Akari Asai
claimed_verified
2023-10-19T08:10:15.751Z
6266e0cb7a1f5a1562c4e86e
https://cdn-avatars.huggingface.co/v1/production/uploads/6266e0cb7a1f5a1562c4e86e/kkJZPssa76mdRPnKH-q3e.jpeg
Akari Asai
user
akariasai
6530af4370cebad89be236fc
Zeqiu Wu
admin_assigned
2023-10-19T08:10:39.980Z
647d335836e109abce3ffb79
/avatars/fee73f1e15502ca51f8a7a928f653d31.svg
Zeqiu Wu
user
zeqiuwu1
6530af4370cebad89be236fd
Yizhong Wang
admin_assigned
2023-10-19T08:10:47.390Z
6269be67d1ac0cde592aba29
https://cdn-avatars.huggingface.co/v1/production/uploads/6269be67d1ac0cde592aba29/EP3zZbD7-jWK9ITvHGbnZ.jpeg
Yizhong Wang
user
yizhongw
6530af4370cebad89be236fe
Avirup Sil
6530af4370cebad89be236ff
Hannaneh Hajishirzi
Self-RAG: Learning to Retrieve, Generate, and Critique through
  Self-Reflection
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.
Self-Reflective Retrieval-Augmented Generation (Self-RAG) enhances large language models by adaptively retrieving and reflecting on passages, improving factuality and performance across various tasks.
retrieval-augmented generation Self-Reflective Retrieval-Augmented Generation Self-RAG reflection tokens fact verification long-form generations Open-domain QA reasoning