Summary of Ctrla: Adaptive Retrieval-augmented Generation Via Inherent Control, by Huanshuo Liu and Hao Zhang and Zhijiang Guo and Jing Wang and Kuicai Dong and Xiangyang Li and Yi Quan Lee and Cong Zhang and Yong Liu
CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
by Huanshuo Liu, Hao Zhang, Zhijiang Guo, Jing Wang, Kuicai Dong, Xiangyang Li, Yi Quan Lee, Cong Zhang, Yong Liu
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Retrieval-augmented generation (RAG) is a technique that leverages large language models (LLMs) with external knowledge to mitigate hallucinations. Adaptive RAG enhances this approach by dynamically retrieving information during generation, only when the query exceeds LLM’s internal knowledge. The authors propose a representation-based framework, called , which extracts features representing honesty and confidence directions of LLMs to control behavior and guide retrieval timing decisions. They also design a query formulation strategy for adaptive retrieval. Experimental results show that outperforms existing methods on diverse tasks, with honesty steering making LLMs more honest and confidence monitoring being a promising indicator of retrieval quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using computers to help us learn new things by combining what we already know with new information from the internet. This paper is about finding ways to make this process better by giving computers more control over what they learn from the internet. The authors came up with a new way to do this called , which helps computers make sure they’re not making mistakes or learning things that aren’t true. They tested their idea and found that it works better than other methods on different tasks. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
