Ruiyang Ren

According to our database1, Ruiyang Ren authored at least 14 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents.
CoRR, 2024

REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering.
CoRR, 2024

The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models.
CoRR, 2024

2023
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation.
CoRR, 2023

A Survey of Large Language Models.
CoRR, 2023

A Thorough Examination on Zero-shot Dense Retrieval.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

TOME: A Two-stage Approach for Model-based Retrieval.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022
Dense Text Retrieval based on Pretrained Language Models: A Survey.
CoRR, 2022

2021
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020
Fine-Grained Privacy Detection with Graph-Regularized Hierarchical Attentive Representation Learning.
ACM Trans. Inf. Syst., 2020

RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering.
CoRR, 2020

Sequential Recommendation with Self-Attentive Multi-Adversarial Network.
Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, 2020


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