Hugging Face Trending Papers
RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale
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arXiv Machine Learning
Sep 22
RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale
arXiv:2609.24464v1 Announce Type: new Abstract: Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial pr...
arXiv AI
Jul 1
One Retrieval to Cover Them All: Co-occurrence-Aware Knowledge Base Reorganization for Session-Level RAG
arXiv:2606. 31156v1 Announce Type: cross Abstract: RAG systems retrieve documents optimized for answering one query at a time.
arXiv Computation and Language
Sep 3
Incremental Pooled LLM Evaluation for Cost-Effective Retrieval Model Selection
The paper proposes an incremental pooled LLM evaluation method for selecting retrieval models in production Retrieval-Augmented Generation (RAG) systems. By having a language model judge the union of documents retrieved by current candidates and expanding the pool only with new documents from added systems, the approach reuses judgments across all systems. Experiments on four benchmarks and a financial news QA deployment show strong correlation with gold-standard rankings, high preservation of pairwise orderings, and significant cost savings—up to 4.9× lower evaluation cost and 65–80% judgment reuse.
arXiv Machine Learning
Jul 23
Efficient Clustering with Provable Guardrails for LLM Inference at Scale
arXiv:2607. 19704v1 Announce Type: new Abstract: Scaling LLM-based applications to millions of users is bottlenecked by the inference cost and latency of modern foundation models.
arXiv Machine Learning
1d ago
Which LLM to pick? Online Active Model Selection for Large Language Models
The paper introduces ONLINE LLM PICKER, a framework for active model selection of large language models in streaming settings. It selects the most informative prompts for annotation within a limited budget, enabling the identification of the best or near‑best model among many candidates. Experiments on 10 datasets and over 130 language models show up to 71.67% savings in annotation cost and a reduction in regret by up to 2.51× when using the chosen model for sequential generation.
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.