Summary of C-rag: Certified Generation Risks For Retrieval-augmented Language Models, by Mintong Kang et al.
C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models
by Mintong Kang, Nezihe Merve Gürel, Ning Yu, Dawn Song, Bo Li
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 The paper investigates the trustworthiness of large language models (LLMs) and retrieval-augmented language models (RAGs), proposing a framework called C-RAG to certify the generation risks of RAGs. The authors aim to answer three key questions: whether RAGs can reduce generation risks, how to provide guarantees on the generation risks of RAGs and vanilla LLMs, and what conditions enable RAG models to achieve lower generation risks. By providing conformal risk analysis for RAG models, the authors establish an upper confidence bound of generation risks and demonstrate that RAGs can achieve a lower conformal generation risk than single LLMs under certain conditions. Empirical results on four NLP datasets using four state-of-the-art retrieval models support the soundness and tightness of the proposed framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models more trustworthy by showing that they’re not just making things up. Right now, these models can get confused and make false information. The researchers want to know if a new type of model called RAG (retrieval-augmented) can be less likely to make mistakes. They also want to figure out how to guarantee that the RAG models are doing a good job. To do this, they came up with a way to measure the risk of the RAG models making false information. The results show that under certain conditions, these new RAG models can be more trustworthy than regular language models. |
Keywords
» Artificial intelligence » Nlp » Rag
