Active learning applied to noisy LLM pairwise judgments improves NDCG@10 per call in budget-constrained reranking and enables unbiased aggregation via a randomized-direction single-call oracle.
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PRISM shows that prompt engineering and selective ICL often outperform complex multi-agent systems on financial retrieval benchmarks while remaining training-free and achieving competitive NDCG@5 scores.
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Active Learners as Efficient PRP Rerankers
Active learning applied to noisy LLM pairwise judgments improves NDCG@10 per call in budget-constrained reranking and enables unbiased aggregation via a randomized-direction single-call oracle.
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PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval
PRISM shows that prompt engineering and selective ICL often outperform complex multi-agent systems on financial retrieval benchmarks while remaining training-free and achieving competitive NDCG@5 scores.