arXiv AI
Jun 2
Test-Time Training for Zero-Resource Dense Retrieval Reranking
arXiv:2606. 01070v1 Announce Type: cross Abstract: Dense retrievers excel at first-stage candidate generation but lack effective reranking in zero-resource settings.
When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking
arXiv:2606. 31087v1 Announce Type: cross Abstract: Few-shot selection typically assumes that reranking retrieved examples always improves performance.
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arXiv Machine Learning
Jun 29
Benchmarking on Tasks That Matter: Dataset Selection for Preserving Model Rankings
arXiv:2606. 27997v1 Announce Type: new Abstract: Benchmarks of machine learning models often include many datasets, making evaluation expensive.
arXiv AI
Jun 16
Spokes: Optimizing for Diverse Pretraining Data Selection
arXiv:2606. 15216v1 Announce Type: cross Abstract: Diversity plays a critical role in data selection, improving performance under fixed data budgets by reducing redundancy and repetition.
arXiv Machine Learning
Jun 5
Statistically Reliable LLM-Based Ranking Evaluation via Prediction-Powered Inference
arXiv:2606. 05308v1 Announce Type: new Abstract: With PRECISE, we extended Prediction-Powered Inference to produce bias-corrected estimates of ranking evaluation metrics by combining a small human-labeled set with a large LLM-judged set.
arXiv Machine Learning
Jul 21
Quantifying Ranking Uncertainty in LLM Benchmarks
arXiv:2607. 16259v1 Announce Type: new Abstract: Pretrained models are typically ranked on multi-task leaderboards to assess their effectiveness across diverse tasks.
arXiv AI
6d ago
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
arXiv:2605. 04495v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily estimate query--document relevance.
arXiv AI
Jun 8
On the importance of multiple training seeds for evaluating machine unlearning
arXiv:2510. 26714v5 Announce Type: replace-cross Abstract: Machine unlearning aims to remove the influence of certain data points from a trained model without costly retraining.
arXiv Machine Learning
Jun 30
Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation
arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.
arXiv Machine Learning
Jul 28
Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models
arXiv:2607. 23052v1 Announce Type: cross Abstract: Dual-encoder vision-language models (VLMs) expose a similarity interface that enables zero-shot retrieval but fails compositional constraints: queries like "umbrella and no person" retrieve images containing both, even when concept detection is reliable.
arXiv AI
Jun 2
Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
arXiv:2606. 01400v1 Announce Type: cross Abstract: Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming.
arXiv AI
1d ago
What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering
arXiv:2608. 14841v1 Announce Type: new Abstract: Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typically follows retrieve-then-read pipelines.
arXiv Machine Learning
Jun 5
FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment
arXiv:2602. 02680v3 Announce Type: replace Abstract: The growing scale of deep neural networks, encompassing large language models (LLMs) and vision transformers (ViTs), has made training from scratch prohibitively expensive and deployment increasingly costly.