Promptriever is the first instruction-trained dense retriever, achieving state-of-the-art results with nearly 500k instruction-augmented pairs.

Orion Weller, Benjamin Van Durme, Dawn Lawrie et al.

TRC Bench evaluates 19 LLMs and organizes RAG trustworthiness into six dimensions.

Yujia Zhou, Wenbo Zhang, Jingying Shao et al.

Proposes multi-category negative sampling strategies, enhancing detection of hard negatives in recommendation models.

Haokai Ma, Ruobing Xie, Lei Meng et al.

Proposes URE for unbiased Recall@K estimation in recommendation evaluation, correcting bias from randomly exposed data.

Chengbing Wang, Wentao Shi, Jizhi Zhang et al.

Proposes a Nash welfare-based fair reciprocal recommendation algorithm balancing total matches and opportunity envy-freeness.

Yoji Tomita, Tomohiki Yokoyama

LARR integrates domain knowledge and contrastive learning to enhance real-time scene semantic understanding for CTR prediction.

Zhizhong Wan, Bin Yin, Junjie Xie et al.

Proposes Contextual Dual Learning Algorithm with Listwise Distillation (CDLA-LD) to address position and contextual biases in unbiased learning to rank, improving real-world performance.

Lulu Yu, Keping Bi, Shiyu Ni et al.

Combining LLMs with web retrieval improves misinformation detection by 20%.

Jacob-Junqi Tian, Hao Yu, Yury Orlovskiy et al.

Enhancing students' higher-order cognitive skills by integrating exploratory search with Kolb's learning model.

Yiming Luo, Patrick Cheong-Iao Pang, Shanton Chang

GradCraft uses dynamic gradient magnitude adjustment and global direction projection to improve multi-task recommendation models' gradient balance and performance.

Yimeng Bai, Yang Zhang, Fuli Feng et al.

SETO enriches samples via temporary operations on subsequences, effectively addressing data sparsity.

Shu Chen, Jinwei Luo, Weike Pan et al.

This study shows that advanced language models, via linear mapping, outperform traditional ID-based recommenders, revealing a space isomorphism and implicit collaborative signals.

Leheng Sheng, An Zhang, Yi Zhang et al.

ColPali employs vision-language models with multi-vector embeddings and late interaction to enhance document retrieval, outperforming traditional methods by 20% on ViDoRe.

Manuel Faysse, Hugues Sibille, Tony Wu et al.

Introduces Ragnarök, a reusable end-to-end RAG framework, with MS MARCO V2.1, establishing the TREC 2024 RAG track; baselines include GPT-4o and Command R+.

Ronak Pradeep, Nandan Thakur, Sahel Sharifymoghaddam et al.

DemoRank improves demonstration selection for LLMs in ranking tasks using dependency-aware reranking, significantly boosting performance.

Wenhan Liu, Yutao Zhu, Zhicheng Dou

FIRST method improves reranking efficiency by 50% using single token decoding on the BEIR benchmark.

Revanth Gangi Reddy, JaeHyeok Doo, Yifei Xu et al.

EAGER introduces a two-stream generative recommender integrating behavior and semantic info, achieving superior performance.

Ye Wang, Jiahao Xun, Minjie Hong et al.

Proposes RecBLR with behavior-dependent linear recurrent units, achieving high performance, training speed, and low inference cost.

Chengkai Liu, Jianghao Lin, Hanzhou Liu et al.

TourRank optimizes document ranking using tournament mechanisms, enhancing performance and reducing costs.

Yiqun Chen, Qi Liu, Yi Zhang et al.

Proposes Softmax-DPO (S-DPO) to enhance recommendation by leveraging multi-negative samples and softmax ranking, significantly outperforming baselines.

Yuxin Chen, Junfei Tan, An Zhang et al.