analiza aiqbench
The paper proposes DMReranker, a zero-shot search reranker that splits an instruction into typed clauses and scores each with a small judge model. It reports a macro WISE of 50.9 against GPT-4o's 33.5 on InfoSearch, but GPT-4o still leads on raw-score SICR (32.1 vs 19.2). The authors attribute that gap to how probability products behave under raw-score comparison, and their rank-based SICR of 67.0 is self-reported and not yet independently checked. For readers, the useful takeaway is that decomposing instructions into small judge questions may be a cheap way to improve instruction-following in retrieval, provided the evaluation metric is handled carefully.
Every Clause Counts: Instruction-Following Reranking by Multiplying Small, Answerable Questions
arXiv:2611.03179v1 Announce Type: cross Abstract: People often attach instructions to their search queries, asking for pages written for beginners, documents in a certain language or official sources only. Most rerankers were trained to judge topical relevance and treat such instructions as a few extra words. On the InfoSearch benchmark, even GPT-4o used as a list-wise reranker reaches an average WISE (Weighted Instruction Sensitivity Evaluation) of only 33.5. We present DMReranker (Decision-Model Reranker), a zero-shot reranker that splits an instructed query into a topic and a short list of typed clauses. A small judge model answers one narrow question per clause, and the answer probabilities are multiplied into a single score. Nothing is trained, and the configuration was frozen before the test runs. Reranking the same top-100 candidates as the published baselines, DMReranker reaches a macro WISE of 50.9 over all six InfoSearch dimensions while keeping original-mode nDCG@10 at 82.9. It trails GPT-4o on the stricter SICR (Strict Instruction Compliance Ratio) metric, 19.2 against 32.1. We show that this gap follows from how a product of probabilities behaves when SICR compares raw scores across runs. Computed from ranks, our SICR is 67.0. On the same judge, splitting the instruction raises WISE from 32.9 to 47.6 over a single combined question, while raw-score SICR falls. Scoring all six dimensions cost about $10 at list price.
Abstract:People often attach instructions to their search queries, asking for pages written for beginners, documents in a certain language or official sources only. Most rerankers were trained to judge topical relevance and treat such instructions as a few extra words. On the InfoSearch benchmark, even GPT-4o used as a list-wise reranker reaches an average WISE (Weighted Instruction Sensitivity Evaluation) of only 33.5. We present DMReranker (Decision-Model Reranker), a zero-shot reranker that splits an instructed query into a topic and a short list of typed clauses. A small judge model answers one narrow question per clause, and the answer probabilities are multiplied into a single score. Nothing is trained, and the configuration was frozen before the test runs. Reranking the same top-100 candidates as the published baselines, DMReranker reaches a macro WISE of 50.9 over all six InfoSearch dimensions while keeping original-mode nDCG@10 at 82.9. It trails GPT-4o on the stricter SICR (Strict Instruction Compliance Ratio) metric, 19.2 against 32.1. We show that this gap follows from how a product of probabilities behaves when SICR compares raw scores across runs. Computed from ranks, our SICR is 67.0. On the same judge, splitting the instruction raises WISE from 32.9 to 47.6 over a single combined question, while raw-score SICR falls. Scoring all six dimensions cost about $10 at list price.
Information Retrieval (cs.IR); Computation and Language (cs.CL)
arXiv:2611.03179 [cs.IR]
(or arXiv:2611.03179v1 [cs.IR] for this version)
https://doi.org/10.48550/arXiv.2611.03179 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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