Research map / Label-efficient and robust learning / Domain adaptation and generalization
A Domain Generalization Perspective on Listwise Context Modeling
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use QILCM instead of the strongest baseline | Ranking models · Airline Itinerary | P@1: 0.2618 -> 0.2833 | Helped |
| use QILCM instead of the strongest baseline | Ranking models · Microsoft 30k | NDCG@1: 0.4973 -> 0.5447 | Helped |
| use QILCM instead of the strongest baseline | Ranking models · Istella-S | NDCG@1: 0.6873 -> 0.7023 | Helped |
| use attention-weighted pooling and statistics instead of average pooling and standard statistics (ablation) | QILCM · Airline Itinerary | P@1: 0.2749 -> 0.2833 | Helped |
| use query confusion loss (ablation) | QILCM · Airline Itinerary | P@1: 0.2762 -> 0.2833 | Helped |
| use query confusion loss and query normalization layer (ablation) | QILCM · Airline Itinerary | P@1: 0.2694 -> 0.2833 | Helped |
| use attention-weighted pooling and statistics instead of average pooling and standard statistics (ablation) | QILCM · Microsoft 30k | NDCG@1: 0.5287 -> 0.5447 | Helped |
| use query confusion loss (ablation) | QILCM · Microsoft 30k | NDCG@1: 0.5359 -> 0.5447 | Helped |
| use query confusion loss and query normalization layer (ablation) | QILCM · Microsoft 30k | NDCG@1: 0.5139 -> 0.5447 | Helped |
| use attention-weighted pooling and statistics instead of average pooling and standard statistics (ablation) | QILCM · Istella-S | NDCG@1: 0.6966 -> 0.7023 | Helped |
| use query confusion loss (ablation) | QILCM · Istella-S | NDCG@1: 0.6982 -> 0.7023 | Helped |
| use query confusion loss and query normalization layer (ablation) | QILCM · Istella-S | NDCG@1: 0.6837 -> 0.7023 | Helped |
Closest papers by meaning
- Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers (ICLR 2025 · similarity 0.86)
- AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking (NeurIPS 2025 · similarity 0.86)
- Scalable In-context Ranking with Generative Models (NeurIPS 2025 · similarity 0.85)
- RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs (NeurIPS 2024 · similarity 0.85)
- Learning to Rank in Generative Retrieval (AAAI 2024 · similarity 0.85)
- Contrastive Retrieval Heads for Improved Attention-Based Reranking (NeurIPS 2026 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.