AUTOMATED REASONING-INTENSIVE TRAINING DATA SYNTHESIS FRAMEWORK

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Challenge:	Existing rerankers perform poorly in complex ranking scenarios due to the scarcity of reasoning-intensive training data.
Approach:	They propose an automated reasoning-intensive training framework which generates high-quality training labels from training queries and passages.
Outcome:	The proposed model outperforms baselines significantly and achieves much lower latency than the pointwise reranker.

Papers with automated reasoning-intensive training data synthesis framework