Summary of Promptrefine: Enhancing Few-shot Performance on Low-resource Indic Languages with Example Selection From Related Example Banks, by Soumya Suvra Ghosal et al.
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks
by Soumya Suvra Ghosal, Soumyabrata Pal, Koyel Mukherjee, Dinesh Manocha
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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 This paper presents a novel approach called PromptRefine for selecting the most optimal few-shot demonstrations in Large Language Models (LLMs) for in-context learning (ICL). The proposed method leverages auxiliary example banks from related high-resource Indic languages and employs multi-task learning techniques to align language-specific retrievers. This enables effective cross-language retrieval, which is particularly important in low-resource Indic languages where the scarcity of ground-truth data complicates the selection process. The authors evaluate their approach on four text generation tasks using state-of-the-art LLMs such as LLAMA-3.1-8B and demonstrate that PromptRefine significantly outperforms existing frameworks for retrieving examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about finding the best way to help computers learn new things quickly, even if they don’t have much training data. The authors came up with a new method called PromptRefine that uses information from related languages to choose the most helpful examples. This helps computers learn faster and more accurately, especially when there’s not much data available. The researchers tested their approach on four different tasks and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Few shot » Llama » Multi task » Text generation
