Papers by Florin Cuconasu
The Distracting Effect: Understanding Irrelevant Passages in RAG (2025.acl-long)
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| Challenge: | Existing methods to detect and use hard distracting passages in RAG can cause problems . retrieved passages contain irrelevant but semantically related information that may mislead the LLM . |
| Approach: | They propose a method to identify and use hard distracting passages to improve RAG . they find that adding retrieved passages is found to ground the LLM response . |
| Outcome: | The proposed method achieves up to 7.5% increase in answering accuracy compared to fine-tuned datasets. |
Do RAG Systems Really Suffer From Positional Bias? (2025.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
| Approach: | They propose to use a retrieval algorithm to add passages from an external corpus to the LLM prompt to improve the factual accuracy of LLMs. |
| Outcome: | The proposed approach improves the factual accuracy of LLMs on knowledgeintensive tasks by including in the prompt passages retrieved from an external corpus. |
Redefining Retrieval Evaluation in the Era of LLMs (2026.eacl-long)
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| Challenge: | Traditional IR metrics assume that humans examine documents sequentially with diminishing attention to lower ranks. |
| Approach: | They propose a utility-based annotation schema that quantifies positive contribution of relevant passages and negative impact of distracting ones. |
| Outcome: | The proposed metric improves correlation with the end-to-end answer accuracy by up to 36% compared to traditional metrics. |