Papers by Lihi Shalmon
More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Models by leveraging relevant external documents during generation. |
| Approach: | They evaluate various language models on custom datasets derived from QA tasks . they keep context length and position of relevant information constant while varying the number of documents . |
| Outcome: | The proposed method improves the accuracy of large language models by leveraging external documents . increasing document count reduces performance by up to 20%, the authors find . |