Glance: The Generator Is Its Own Reranker
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabGlance proposes using a single 9B generator with a lightweight LoRA decision adapter and pointer head to rerank retrieved passages. All candidate passages are prefilled once into a shared cache, and short relevance probes branch from it to produce one score per passage. The paper reports nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, versus 0.721 and 0.326 for bge-reranker-v2-m3, after 39 minutes of MS MARCO fine-tuning. On 300 HotpotQA questions, the selected context improves answer F1 by 0.045 over cross-encoder-selected context. Evaluation covers two retrieval datasets, one QA dataset and one backbone family.
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- Primary sourceHugging Face Daily Papers
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Glance proposes using a single 9B generator with a lightweight LoRA decision adapter and pointer head to rerank retrieved passages. All candidate passages are prefilled once into a shared cache, and short relevance probes branch from it to produce one score per passage. The paper reports nDCG@10 of 0.741 on SciFact and 0.345 on NFCorpus, versus 0.721 and 0.326 for bge-reranker-v2-m3, after 39 minutes of MS MARCO fine-tuning. On 300 HotpotQA questions, the selected context improves answer F1 by 0.045 over cross-encoder-selected context. Evaluation covers two retrieval datasets, one QA dataset and one backbone family.
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Evaluation covers only two retrieval datasets, one QA dataset and one backbone family, and the authors describe these as initial results. Source claims not independently checked.
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Key findings from the stored brief
- A 9B backbone with a LoRA adapter and pointer head scores passages from one shared prefill.
- On SciFact and NFCorpus, nDCG@10 is 0.741 and 0.345 versus 0.721 and 0.326 for bge-reranker-v2-m3.
- On 300 HotpotQA questions, answer F1 improves by 0.045 over cross-encoder-selected context.
- Reranking quality keeps rising up to 200 candidates scored from one shared prefill.
Evaluation covers only two retrieval datasets, one QA dataset and one backbone family, and the authors describe these as initial results. Source claims not independently checked.
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Why this may matter
If generator-based reranking holds up, RAG systems could serve one resident model instead of separate reranker and generator models, reducing memory duplication and repeated passage processing.
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Evaluation covers only two retrieval datasets, one QA dataset and one backbone family, and the authors describe these as initial results. Source claims not independently checked.
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