Copertina

Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Zeyuan, Wu, Haiyan, Wu, Kaixin, Chen, Wei, Zhong, Mingjie, Xu, Jia, Liu, Zhongyi, Zhang, Wei
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Information Retrieval Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning
di: Liu, Hong, et al.
Pubblicazione: (2024)

MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation
di: Pan, Xiangchen, et al.
Pubblicazione: (2026)

CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search
di: Wu, Kaixin, et al.
Pubblicazione: (2024)

Alleviating LLM-based Generative Retrieval Hallucination in Alipay Search
di: Shen, Yedan, et al.
Pubblicazione: (2025)

Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models
di: Zhou, Jianqun, et al.
Pubblicazione: (2024)

Towards an In-Depth Comprehension of Case Relevance for Better Legal Retrieval
di: Li, Haitao, et al.
Pubblicazione: (2024)

Generative Retrieval Meets Multi-Graded Relevance
di: Tang, Yubao, et al.
Pubblicazione: (2024)

PairDistill: Pairwise Relevance Distillation for Dense Retrieval
di: Huang, Chao-Wei, et al.
Pubblicazione: (2024)

Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness
di: Zhao, Xinran, et al.
Pubblicazione: (2024)

Pattern-wise Transparent Sequential Recommendation
di: Ma, Kun, et al.
Pubblicazione: (2024)

Topic-Specific Classifiers are Better Relevance Judges than Prompted LLMs
di: Gienapp, Lukas, et al.
Pubblicazione: (2025)

SelfRACG: Enabling LLMs to Self-Express and Retrieve for Code Generation
di: Dong, Qian, et al.
Pubblicazione: (2025)

Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models
di: Chen, Catherine, et al.
Pubblicazione: (2024)

Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction
di: Xu, Xiang, et al.
Pubblicazione: (2024)

Fine-Grained Guidance for Retrievers: Leveraging LLMs' Feedback in Retrieval-Augmented Generation
di: Liu, Yuhang, et al.
Pubblicazione: (2024)

Carbon Footprint Accounting Driven by Large Language Models and Retrieval-augmented Generation
di: Wang, Haijin, et al.
Pubblicazione: (2024)

Controlled Retrieval-augmented Context Evaluation for Long-form RAG
di: Ju, Jia-Huei, et al.
Pubblicazione: (2025)

Towards Copyright Protection for Knowledge Bases of Retrieval-augmented Language Models via Reasoning
di: Guo, Junfeng, et al.
Pubblicazione: (2025)

Unified Supervision for Walmart's Sponsored Search Retrieval via Joint Semantic Relevance and Behavioral Engagement Modeling
di: Desai, Shasvat, et al.
Pubblicazione: (2026)

DMGIN: How Multimodal LLMs Enhance Large Recommendation Models for Lifelong User Post-click Behaviors
di: Wei, Zhuoxing, et al.
Pubblicazione: (2025)

Boosting Data Utilization for Multilingual Dense Retrieval
di: Huang, Chao, et al.
Pubblicazione: (2025)

Mitigating the Threshold Priming Effect in Large Language Model-Based Relevance Judgments via Personality Infusing
di: Chen, Nuo, et al.
Pubblicazione: (2025)

Middleman Bias in Advertising: Aligning Relevance of Keyphrase Recommendations with Search
di: Dey, Soumik, et al.
Pubblicazione: (2025)

Identifying Key Terms in Prompts for Relevance Evaluation with GPT Models
di: Choi, Jaekeol
Pubblicazione: (2024)

Direct Retrieval-augmented Optimization: Synergizing Knowledge Selection and Language Models
di: Shi, Zhengliang, et al.
Pubblicazione: (2025)

KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation
di: Meng, Zeyuan, et al.
Pubblicazione: (2025)

LLMJudge: LLMs for Relevance Judgments
di: Rahmani, Hossein A., et al.
Pubblicazione: (2024)

NoteLLM: A Retrievable Large Language Model for Note Recommendation
di: Zhang, Chao, et al.
Pubblicazione: (2024)

Data, Not Model: Explaining Bias toward LLM Texts in Neural Retrievers
di: Huang, Wei, et al.
Pubblicazione: (2026)

Behavior Modeling Space Reconstruction for E-Commerce Search
di: Wang, Yejing, et al.
Pubblicazione: (2025)

Optimizing Retrieval Components for a Shared Backbone via Component-Wise Multi-Stage Training
di: Li, Yunhan, et al.
Pubblicazione: (2026)

Prompt Tuning on Graph-augmented Low-resource Text Classification
di: Wen, Zhihao, et al.
Pubblicazione: (2023)

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation
di: Liu, Peiyang, et al.
Pubblicazione: (2026)

Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
di: Ma, Shengjie, et al.
Pubblicazione: (2024)

Is Relevance Propagated from Retriever to Generator in RAG?
di: Tian, Fangzheng, et al.
Pubblicazione: (2025)

Retrieval-augmented Prompt Learning for Pre-trained Foundation Models
di: Chen, Xiang, et al.
Pubblicazione: (2025)

FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models
di: Chen, Zhuo, et al.
Pubblicazione: (2025)

K-CARE: Knowledge-driven Symmetrical Contextual Anchoring and Analogical Prototype Reasoning for E-commerce Relevance
di: Yifei, Chen, et al.
Pubblicazione: (2026)

RAG-Match: Retrieval-Augmented Knowledge Injection and Hierarchical Reasoning for Calibrated Semantic Relevance
di: Jiang, Hengjun, et al.
Pubblicazione: (2026)

Align$^3$GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation
di: Ye, Wencai, et al.
Pubblicazione: (2025)