Summary of Conflare: Conformal Large Language Model Retrieval, by Pouria Rouzrokh et al.
CONFLARE: CONFormal LArge language model REtrieval
by Pouria Rouzrokh, Shahriar Faghani, Cooper U. Gamble, Moein Shariatnia, Bradley J. Erickson
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 introduces a four-step framework for applying conformal prediction to quantify retrieval uncertainty in Retrieval-Augmented Generation (RAG) frameworks. RAG enables large language models (LLMs) to retrieve relevant information from a knowledge base and incorporate it into the context for generating responses, mitigating hallucinations and allowing for updating of knowledge without retraining the LLM. However, quantifying uncertainty in the retrieval process is crucial for ensuring trustworthiness. The framework first constructs a calibration set of questions answerable from the knowledge base, then compares question embeddings against document embeddings to identify relevant document chunks containing answers. Similarity scores are analyzed to determine a threshold, and during inference, all chunks with similarity exceeding this threshold are retrieved to provide context to the LLM, ensuring true answers are captured with confidence level (1-α). A Python package is provided for users to implement the workflow using LLMs without human intervention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn to retrieve information from a knowledge base and use it to answer questions. They can even update what they know without needing to be retrained! But sometimes, this process doesn’t work perfectly, so we need to figure out how likely the answer is correct. This paper shows us how to do that by creating a special system that analyzes which parts of the knowledge base are most relevant and uses those to give answers. It’s like having a super smart librarian who can help machines find the right information! |
Keywords
» Artificial intelligence » Inference » Knowledge base » Rag » Retrieval augmented generation
