arXiv Machine Learning

MUSS: Multilevel Subset Selection for Relevance and Diversity

arXiv:2503. 11126v4 Announce Type: replace Abstract: The problem of relevant and diverse subset selection has a wide range of applications, including recommender systems and retrieval-augmented generation (RAG).

arXiv AI

Jun 30

Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation

arXiv:2606. 29328v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) typically treats context selection as ranking chunks against a single query embedding.

By Bingxue Zhang, Jianying Jia, Feida Zhu

rag multimodal benchmarks

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.

By Clarence Lee, Yejin Choi, Luke Zettlemoyer, Pang Wei Koh, Hai Leong Chieu

arXiv Machine Learning

Jul 14

Diversified Multinomial Logit Contextual Bandits

arXiv:2607. 11684v1 Announce Type: cross Abstract: Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial bandits encode diversity in rewards but lack structured choice probabilities.

By Heesang Ann, Taehyun Hwang, Min-hwan Oh

reinforcement-learning

arXiv AI

Jul 14

Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks

arXiv:2607. 09739v1 Announce Type: new Abstract: We study LLM benchmark coreset selection: selecting a small subset of prompts over multiple benchmarks whose induced model scores and rankings approximate those obtained from the full benchmark suite.

By Jihan Yao, Gantavya Bhatt, Arnav Das, Peter Jin, Ke Bao, Qiaolin Yu, Khushi Bhardwaj, Chang Su, Jialei Wang, Yikai Zhu, Sugam Devare, Damon Mosk-Aoyama, Zhen Dong, Venkat Krishna Srinivasan, Yineng Zhang, Oleksii Kuchaiev, Jiantao Jiao, Banghua Zhu, Jeff Bilmes

llms rag benchmarks

arXiv AI

Jun 16

Utility-Diversity Aware Online Batch Selection for LLM Supervised Fine-tuning

arXiv:2510. 16882v4 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) is a commonly used technique to adapt large language models (LLMs) to downstream tasks.

By Heming Zou, Yixiu Mao, Yun Qu, Qi Wang, Xiangyang Ji

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

Jun 9

Constrained user-item allocation for e-commerce marketing campaigns

arXiv:2606. 09623v1 Announce Type: new Abstract: When running marketing campaigns, retailers must decide which products to promote and which users to target.

By Maja Lindstr\"om, Natalija Glisovic, Jan von Pichowski, Tommy L\"ofstedt, Martin Rosvall

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 7

Relevance-Based Embeddings: Lightweight Candidate Retrieval via Heavy-Ranker Calls

arXiv:2607. 03515v1 Announce Type: cross Abstract: In many machine learning applications, the most relevant items for a query should be efficiently retrieved.

By Kirill Shevkunov, Andrey Ploskonosov, Liudmila Prokhorenkova

rag

arXiv AI

Jul 3

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.

By Yuhang Wu, Xiangqing Shen, Fanfan Wang, Cangqi Zhou, Zhen Wu, Xinyu Dai, Rui Xia

llms rag reinforcement-learning benchmarks

arXiv AI

Jul 8

InfluMatch: Frontier-Quality KOL Search at 4B-Model Cost

arXiv:2607. 05968v1 Announce Type: cross Abstract: Matching influencers (KOLs) to free-form, multi-part Thai marketing criteria is today served either by keyword search over structured profiles, which misses semantic fit, or by prompting frontier LLMs over every candidate, which is accurate but slow and expensive.

By Krittanon Kaewtawee, Petmongkon Pornpichitsuwan, Natchaya Temyingyong, Nutnicha Laplamoon, Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn

llms fine-tuning

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.

By Rostislav Gusev, Alexey Zaytsev

nlp benchmarks

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.

By Denica Kjorvezir, Marko Djukanovi\'c, Ana Gjorgjevikj, Gjorgjina Cenikj, Tome Eftimov

llms rag benchmarks

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.

By Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher), Yicheng Wang (Independent Researcher)

llms rag benchmarks

Related stories