Papers by Kamel Charaf
GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Retrieval-Augmented Generation (RAG) enriches prompts with external information, but its reliance on static databases constrains adaptability and can result in irrelevant demonstrations. |
| Approach: | They propose a Generative Retrieval-Aligned Demonstrator (GRAD) that trains an LLM model to generate input-specific concise demonstrations. |
| Outcome: | The proposed model outperforms strong baselines on Qwen2.5-14B across mathematical reasoning and advanced STEM questions. |