How Retrieved Context Shapes Internal Representations in RAG (2026.findings-acl)
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| Challenge: | Retrieval-augmented generation (RAG) is a widely adopted approach for enhancing large language models with external knowledge. |
| Approach: | They analyze how different types of retrieved documents affect the hidden states of large language models and how these internal representation shifts relate to downstream generation behavior. |
| Outcome: | The results show that context relevancy and layer-wise processing influence internal representations, providing explanations of LLMs’ output behaviors and insights for RAG system design. |
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