Summary of Qcg-rerank: Chunks Graph Rerank with Query Expansion in Retrieval-augmented Llms For Tourism Domain, by Qikai Wei et al.
QCG-Rerank: Chunks Graph Rerank with Query Expansion in Retrieval-Augmented LLMs for Tourism Domain
by Qikai Wei, Mingzhi Yang, Chunlong Han, Jingfu Wei, Minghao Zhang, Feifei Shi, Huansheng Ning
First submitted to arxiv on: 4 Nov 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 The proposed QCG-Rerank model addresses the issue of hallucination in Large Language Models (LLMs) by leveraging information retrieval techniques and integrating query expansion and chunk graph construction. This approach enhances semantics by extracting critical information to expand the original query, which is particularly effective in the tourism domain where queries are brief and database contents are diverse. The model iteratively computes transition probabilities based on an initial estimate until convergence, selecting the chunks with the highest score for input into LLMs to generate responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper introduces a new approach called QCG-Rerank that helps Large Language Models (LLMs) provide more accurate and relevant answers. It’s like having a super-smart librarian who can help you find exactly what you’re looking for! |
Keywords
» Artificial intelligence » Hallucination » Semantics
