Summary of Retrieval-augmented Generation in Multilingual Settings, by Nadezhda Chirkova et al.
Retrieval-augmented generation in multilingual settings
by Nadezhda Chirkova, David Rau, Hervé Déjean, Thibault Formal, Stéphane Clinchant, Vassilina Nikoulina
First submitted to arxiv on: 1 Jul 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 Medium Difficulty summary: This paper proposes retrieval-augmented generation (mRAG) in a multilingual setting, leveraging 13 languages to improve large language model (LLM) factuality. The authors investigate the components and adjustments needed for a well-performing mRAG pipeline, highlighting the importance of task-specific prompt engineering for generating user queries in various languages. They also emphasize the need for adjusted evaluation metrics to account for spelling variations in named entities. While the approach shows promise, limitations include code-switching issues in non-Latin alphabet languages, fluency errors, and potential retrieval flaws. The authors release their mRAG baseline pipeline code at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research explores a way to make large language models more accurate by using information from many languages. They test this idea with 13 different languages and find that it works well as long as they create special prompts for each language. The authors also realize that the usual ways we measure how well these models work need to be adjusted because of differences in spelling between languages. While their approach has some problems, like getting stuck on words or finding wrong information, it could help other researchers make more accurate language models in the future. |
Keywords
» Artificial intelligence » Large language model » Prompt » Retrieval augmented generation
