Summary of Bridging the Gap: Dynamic Learning Strategies For Improving Multilingual Performance in Llms, by Somnath Kumar et al.
Bridging the Gap: Dynamic Learning Strategies for Improving Multilingual Performance in LLMs
by Somnath Kumar, Vaibhav Balloli, Mercy Ranjit, Kabir Ahuja, Tanuja Ganu, Sunayana Sitaram, Kalika Bali, Akshay Nambi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 presents novel techniques for enhancing the multilingual performance of large language models (LLMs) without extensive training or fine-tuning. The authors investigate and evaluate diverse languages using popular question-answering datasets, achieving significant improvements in multilingual proficiency. They introduce three key strategies: optimizing prompts for polyglot LLMs, a hybrid approach combining RAG with multilingual embeddings, and a novel learning approach that dynamically selects the optimal prompt strategy, model, and embedding per query. This dynamic adaptation maximizes the efficacy of LLMs across languages, outperforming best static and random strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models understand many different languages better. Right now, these models are great at understanding English and other Latin-based languages, but they struggle with non-Latin scripts like Chinese or Arabic. The researchers found ways to make the models better at understanding all kinds of languages without needing a lot more training or tweaking. They did this by creating special prompts that help the models understand different languages better, combining two techniques to improve results, and developing an approach that picks the best way to answer questions based on the language being used. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Prompt » Question answering » Rag
