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arXiv Machine Learning By Markus Frohmann, Mahdiyar Alavi, Elizabeth Lingg, Navid Rekabsaz

Equal Ranking Quality, Different Decisions: Training Order-Consistent LLM Scorers

Read the original on arXiv Machine Learning →

The paper investigates how the order of candidate documents in a prompt affects the decisions made by large‑language‑model (LLM) scorers, even when their ranking quality is similar. It shows that five scorers with only a 0.010 nDCG@10 difference can produce retained‑set overlaps as low as 0.66–0.84, and that existing rerankers still exhibit significant order dependence. The authors propose Order‑Consistency SFT (OC‑SFT), a training method that reduces this dependence, maintaining ranking quality while improving decision stability across multiple tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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