Summary of Mirage-bench: Automatic Multilingual Benchmark Arena For Retrieval-augmented Generation Systems, by Nandan Thakur et al.
MIRAGE-Bench: Automatic Multilingual Benchmark Arena for Retrieval-Augmented Generation Systems
by Nandan Thakur, Suleman Kazi, Ge Luo, Jimmy Lin, Amin Ahmad
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to evaluating Retrieval-Augmented Generation (RAG) models, which have traditionally relied on heuristic-based metrics. The authors develop MIRAGE-Bench, a standardized arena-based multilingual RAG benchmark for 18 languages, using the MIRACL retrieval dataset and extending it for multilingual generation evaluation. They train a learning-to-rank model as a “surrogate” judge to produce a synthetic arena-based leaderboard, achieving high correlation (Kendall Tau = 0.909) using this approach. The authors benchmark 19 diverse multilingual-focused Large Language Models (LLMs), observing that proprietary and large open-source LLMs currently dominate in multilingual RAG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a new way to compare language models that can generate text in many languages. Instead of relying on human judgments, the authors train a special model to act like a judge, evaluating different language models based on how well they perform on a specific task. This new approach helps create a fair and reliable leaderboard for comparing these models. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
