Papers with reusable and open-source pipeline

1 papers
LimRank: Less is More for Reasoning-Intensive Information Reranking (2025.emnlp-main)

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Challenge: Existing approaches to rerank information require large-scale fine-tuning, which is computationally expensive.
Approach: They propose an open-source pipeline for generating diverse, challenging, and realistic reranking examples.
Outcome: The proposed model performs competitively on two benchmarks, while being trained on less than 5% of the data typically used in prior work.

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