Cover Image

From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Hengran, Zhang, Ruqing, Guo, Jiafeng, de Rijke, Maarten, Fan, Yixing, Cheng, Xueqi
Format:	Preprint
Published:	2023
Subjects:	Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Are Large Language Models Good at Utility Judgments?
by: Zhang, Hengran, et al.
Published: (2024)

Does Generative Retrieval Overcome the Limitations of Dense Retrieval?
by: Zhang, Yingchen, et al.
Published: (2025)

On the Scaling of Robustness and Effectiveness in Dense Retrieval
by: Liu, Yu-An, et al.
Published: (2025)

Bootstrapped Pre-training with Dynamic Identifier Prediction for Generative Retrieval
by: Tang, Yubao, et al.
Published: (2024)

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

Continual Learning for Generative Retrieval over Dynamic Corpora
by: Chen, Jiangui, et al.
Published: (2023)

AdversarialCoT: Single-Document Retrieval Poisoning for LLM Reasoning
by: Song, Hongru, et al.
Published: (2026)

Attack-in-the-Chain: Bootstrapping Large Language Models for Attacks Against Black-box Neural Ranking Models
by: Liu, Yu-An, et al.
Published: (2024)

Robust Neural Information Retrieval: An Adversarial and Out-of-distribution Perspective
by: Liu, Yu-An, et al.
Published: (2024)

On the Robustness of Generative Information Retrieval Models
by: Liu, Yu-An, et al.
Published: (2024)

Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
by: Liu, Yu-An, et al.
Published: (2024)

An Empirical Study of Evaluating Long-form Question Answering
by: Xian, Ning, et al.
Published: (2025)

Listwise Generative Retrieval Models via a Sequential Learning Process
by: Tang, Yubao, et al.
Published: (2024)

An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs
by: Zhang, Hengran, et al.
Published: (2024)

Robust Information Retrieval
by: Liu, Yu-An, et al.
Published: (2024)

VeriCite: Towards Reliable Citations in Retrieval-Augmented Generation via Rigorous Verification
by: Qian, Haosheng, et al.
Published: (2025)

CorpusBrain++: A Continual Generative Pre-Training Framework for Knowledge-Intensive Language Tasks
by: Guo, Jiafeng, et al.
Published: (2024)

Reason to Retrieve: Enhancing Query Understanding through Decomposition and Interpretation
by: Zhong, Yunfei, et al.
Published: (2025)

The Silent Saboteur: Imperceptible Adversarial Attacks against Black-Box Retrieval-Augmented Generation Systems
by: Song, Hongru, et al.
Published: (2025)

TrustRAG: An Information Assistant with Retrieval Augmented Generation
by: Fan, Yixing, et al.
Published: (2025)

Beyond Relevance: Utility-Centric Retrieval in the LLM Era
by: Zhang, Hengran, et al.
Published: (2026)

Generative Retrieval for Book search
by: Tang, Yubao, et al.
Published: (2025)

LLMs as Sparse Retrievers:A Framework for First-Stage Product Search
by: Song, Hongru, et al.
Published: (2025)

Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems
by: Song, Hongru, et al.
Published: (2025)

LLM-Specific Utility: A New Perspective for Retrieval-Augmented Generation
by: Zhang, Hengran, et al.
Published: (2025)

Utility-Focused LLM Annotation for Retrieval and Retrieval-Augmented Generation
by: Zhang, Hengran, et al.
Published: (2025)

A Generative Framework for Personalized Sticker Retrieval
by: Zhou, Changjiang, et al.
Published: (2025)

Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation
by: Zhang, Hengran, et al.
Published: (2025)

Thinking Forward and Backward: Multi-Objective Reinforcement Learning for Retrieval-Augmented Reasoning
by: Wei, Wenda, et al.
Published: (2025)

On the Capacity of Citation Generation by Large Language Models
by: Qian, Haosheng, et al.
Published: (2024)

Training Dense Retrievers with Multiple Positive Passages
by: Wang, Benben, et al.
Published: (2026)

A Comparative Study of Specialized LLMs as Dense Retrievers
by: Zhang, Hengran, et al.
Published: (2025)

C2T-ID: Converting Semantic Codebooks to Textual Document Identifiers for Generative Search
by: Zhang, Yingchen, et al.
Published: (2025)

Retrieval-in-the-Chain: Bootstrapping Large Language Models for Generative Retrieval
by: Zhang, Yingchen, et al.
Published: (2025)

Unleashing the Power of LLMs in Dense Retrieval with Query Likelihood Modeling
by: Zhang, Hengran, et al.
Published: (2025)

Bridging Queries and Tables through Entities in Table Retrieval
by: Li, Da, et al.
Published: (2025)

Reproducibility Analysis and Enhancements for Multi-Aspect Dense Retriever with Aspect Learning
by: Bi, Keping, et al.
Published: (2024)

Tailoring Table Retrieval from a Field-aware Hybrid Matching Perspective
by: Li, Da, et al.
Published: (2025)

Bagging-Based Model Merging for Robust General Text Embeddings
by: Zhang, Hengran, et al.
Published: (2026)

Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrieval
by: Liu, Yu-An, et al.
Published: (2025)