Similarity is Not All You Need: Endowing Retrieval Augmented Generation
  with Multi Layered Thoughts
In recent years, large language models (LLMs) have made remarkable
achievements in various domains. However, the untimeliness and cost of
knowledge updates coupled with hallucination issues of LLMs have curtailed
their applications in knowledge intensive tasks, where retrieval augmented
generation (RAG) can be of help. Nevertheless, existing retrieval augmented
models typically use similarity as a bridge between queries and documents and
follow a retrieve then read procedure. In this work, we argue that similarity
is not always the panacea and totally relying on similarity would sometimes
degrade the performance of retrieval augmented generation. To this end, we
propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented
Generation framework. To begin with, beyond existing similarity oriented
thought, we embrace a small scale utility model that draws supervision from an
LLM for utility oriented thought and further come up with a smarter model by
comprehensively combining the similarity and utility oriented thoughts.
Furthermore, given the fact that the retrieved document set tends to be huge
and using them in isolation makes it difficult to capture the commonalities and
characteristics among them, we propose to make an LLM as a task adaptive
summarizer to endow retrieval augmented generation with compactness-oriented
thought. Finally, with multi layered thoughts from the precedent stages, an LLM
is called for knowledge augmented generation. Extensive experiments on
knowledge-intensive tasks have demonstrated the superiority of MetRag.
6659496e6827c52ec8667980
Chunjing Gan
6659496e6827c52ec8667981
Dan Yang
6659496e6827c52ec8667982
Binbin Hu
6659496e6827c52ec8667983
Hanxiao Zhang
admin_assigned
2024-05-31T10:12:36.859Z
643659b5ad9b9147de282fd9
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HanXiao Zhang
user
nighttrain
6659496e6827c52ec8667984
Siyuan Li
claimed_verified
2024-10-13T20:19:50.551Z
640f7083208821a59b74c757
https://cdn-avatars.huggingface.co/v1/production/uploads/1678735253848-640f7083208821a59b74c757.jpeg
Siyuan Li
user
Lupin1998
6659496e6827c52ec8667985
Ziqi Liu
admin_assigned
2024-05-31T10:13:49.472Z
64264f4885f26ab94af3e1c3
/avatars/db60c953e3d0c9befb1ff96815320ccb.svg
Ziqi Liu
user
ziqiliu
6659496e6827c52ec8667986
Yue Shen
6659496e6827c52ec8667987
Lin Ju
6659496e6827c52ec8667988
Zhiqiang Zhang
6659496e6827c52ec8667989
Jinjie Gu
6659496e6827c52ec866798a
Lei Liang
admin_assigned
2024-05-31T10:14:47.296Z
64588a9caa426bae5e080567
/avatars/76c11bfbe64058014d4b2b89d18ba2e4.svg
Lei Liang
user
square0083
6659496e6827c52ec866798b
Jun Zhou
Similarity is Not All You Need: Endowing Retrieval Augmented Generation
  with Multi Layered Thoughts
In recent years, large language models (LLMs) have made remarkable
achievements in various domains. However, the untimeliness and cost of
knowledge updates coupled with hallucination issues of LLMs have curtailed
their applications in knowledge intensive tasks, where retrieval augmented
generation (RAG) can be of help. Nevertheless, existing retrieval augmented
models typically use similarity as a bridge between queries and documents and
follow a retrieve then read procedure. In this work, we argue that similarity
is not always the panacea and totally relying on similarity would sometimes
degrade the performance of retrieval augmented generation. To this end, we
propose MetRag, a Multi layEred Thoughts enhanced Retrieval Augmented
Generation framework. To begin with, beyond existing similarity oriented
thought, we embrace a small scale utility model that draws supervision from an
LLM for utility oriented thought and further come up with a smarter model by
comprehensively combining the similarity and utility oriented thoughts.
Furthermore, given the fact that the retrieved document set tends to be huge
and using them in isolation makes it difficult to capture the commonalities and
characteristics among them, we propose to make an LLM as a task adaptive
summarizer to endow retrieval augmented generation with compactness-oriented
thought. Finally, with multi layered thoughts from the precedent stages, an LLM
is called for knowledge augmented generation. Extensive experiments on
knowledge-intensive tasks have demonstrated the superiority of MetRag.
MetRag enhances retrieval-augmented generation by integrating similarity and utility-oriented thoughts, employing an LLM as a task adaptive summarizer, and leveraging multi-layered thoughts for knowledge-intensive tasks.
retrieval augmented generation similarity utility model task adaptive summarizer knowledge augmented generation multi-layered thoughts