Papers by Max Schettewi
Embedding-Free RAG (2025.findings-emnlp)
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| Challenge: | Retrieval-Augmented Generation (RAG) is the current state-of-the-art method for mitigating the shortcomings of large language models. |
| Approach: | They propose a model-agnostic approach to retrieval-augmented generation that leverages generalized reasoning abilities of large language models. |
| Outcome: | Embedding-free RAG outperforms existing state-of-the-art methods in a wide range of domains. |