Summary of Embedding-informed Adaptive Retrieval-augmented Generation Of Large Language Models, by Chengkai Huang et al.
Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models
by Chengkai Huang, Yu Xia, Rui Wang, Kaige Xie, Tong Yu, Julian McAuley, Lina Yao
First submitted to arxiv on: 4 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to retrieval-augmented large language models (LLMs) called Adaptive Retrieval-Augmented Generation (ARAG). Unlike previous works, ARAG determines whether the model is knowledgeable on a query by inspecting pre-trained token embeddings, avoiding accessing the pre-training corpus or prompting with additional model inferences. The authors hypothesize that these embeddings capture rich information on the model’s intrinsic knowledge base, enabling efficient retrieval decision-making. Experimental results demonstrate ARAG’s superior performance across various benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers talk smarter by making sure they only look for extra information when they don’t already know the answer. Normally, computers use large language models to figure out what to say next, but sometimes these models aren’t smart enough and need help. The authors came up with a new way to check if the computer knows the answer without having to search through its entire training data. They tested this approach on many different tasks and found it worked really well. |
Keywords
» Artificial intelligence » Knowledge base » Prompting » Retrieval augmented generation » Token
