Papers by Ruiyang Ren
TOME: A Two-stage Approach for Model-based Retrieval (2023.acl-long)
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| Challenge: | Recent research has focused on model-based retrieval, which discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Approach: | They propose a model-based retrieval approach that discards the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
| Outcome: | The proposed approach eliminates the index in the traditional retrieval model and memorizes the candidate corpora using model parameters. |
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2025.naacl-long)
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| Challenge: | Existing large language models (LLMs) show exceptional problem-solving capabilities but struggle with complex reasoning tasks. |
| Approach: | They propose a novel RAG approach that integrates retrieved information to guide tree-based reasoning process based on LLMs. |
| Outcome: | The proposed approach outperforms existing methods in large language models . iteratively plans intermediate sub-queries and answers based on the LLM itself . |
PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021.findings-acl)
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Ruiyang Ren, Shangwen Lv, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent studies only consider query-centric similarity relation when learning the dual-encoder retriever. |
| Approach: | They propose a query-centric and PAssage-centric approach to capture more comprehensive similarity relations for dense passage retrieval. |
| Outcome: | The proposed approach significantly outperforms existing models on both MSMARCO and Natural Questions datasets. |
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models (2024.acl-long)
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| Challenge: | a growing number of researchers are studying the hallucination issue in large language models. |
| Approach: | They propose a hallucination detection benchmark and a method to detect hallucines in LLMs. |
| Outcome: | The proposed method detects hallucinations and mitigates them using different training stages. |
Reinforced Informativeness Optimization for Long-Form Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing reinforcement learning systems lack verifiable reward mechanisms for long-form question answering . current systems lack reliable long-term answers due to lack of factual content . |
| Approach: | They propose a framework for reinforced verifiable informativeness optimization . it defines informativeness as measurable and externally verifier objective for RL . |
| Outcome: | Experiments show that RioRAG achieves higher factual recall and faithfulness . the proposed framework is based on a framework that uses nugget-centric verification with cross-source checks . |
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation (2025.coling-main)
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| Challenge: | Large language models (LLMs) have shown impressive prowess in solving a wide range of tasks with world knowledge, but it remains unclear how well they perceive their factual knowledge boundaries. |
| Approach: | They propose to use a retrieval augmentation approach to enhance LLMs' awareness of factual knowledge boundaries to analyze factual and factual information in open-domain question answering (QA) |
| Outcome: | The proposed method improves LLMs’ QA and judgemental capabilities by integrating supporting documents with the questions. |
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021.emnlp-main)
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| Challenge: | Recent studies show that passage retrieval and passage reranking are important for achieving mutual improvement. |
| Approach: | They propose a unified listwise training approach for passage retrieval and passage reranking that incorporates a retrieval procedure and a hybrid data augmentation strategy. |
| Outcome: | The proposed approach improves on both MSMARCO and Natural Questions datasets. |
REAR: A Relevance-Aware Retrieval-Augmented Framework for Open-Domain Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods to extend knowledge scope of large language models (LLMs) lack internal parametric knowledge, resulting in misusing external knowledge. |
| Approach: | They propose a retrieval-augmented approach that provides LLMs with potentially relevant documents through a module. |
| Outcome: | The proposed approach outperforms existing methods on four open-domain QA tasks. |
A Thorough Examination on Zero-shot Dense Retrieval (2023.findings-emnlp)
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Ruiyang Ren, Yingqi Qu, Jing Liu, Xin Zhao, Qifei Wu, Yuchen Ding, Hua Wu, Haifeng Wang, Ji-Rong Wen
| Challenge: | Recent advances in dense retrieval (DR) models have been shown to be not as competitive as traditional sparse retrieval models in a zero-shot retrieval setting. |
| Approach: | They propose to examine the zero-shot capability of DR models by analyzing key factors related to source training set and potential bias from target dataset. |
| Outcome: | The proposed model is not as competitive as sparse retrieval models in a zero-shot retrieval setting. |
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents (2024.findings-emnlp)
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| Challenge: | Existing research on web search rely on real-user experiments, which can be costly to scale up. |
| Approach: | They propose a user simulation framework with LLM-based agents that can generate unique user profiles at scale. |
| Outcome: | The proposed framework can generate unique user profiles at scale, leading to diverse search behaviors. |
SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis (2025.findings-emnlp)
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Shuang Sun, Huatong Song, Yuhao Wang, Ruiyang Ren, Jinhao Jiang, Junjie Zhang, Fei Bai, Jia Deng, Xin Zhao, Zheng Liu, Lei Fang, Zhongyuan Wang, Ji-Rong Wen
| Challenge: | Existing approaches to deep search training lack high-quality training trajectories, prohibitive computational costs and lack of high-fidelity training data. |
| Approach: | They propose a framework that synthesizes high-quality training data by simulating real user interactions in live web search environments. |
| Outcome: | The proposed framework synthesizes high-quality training data by simulating user interactions in live web search environments. |
RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering (2021.naacl-main)
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Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, Haifeng Wang
| Challenge: | Open-domain question answering uses dense passage retrieval to find answers . however, it is difficult to effectively train a dual-encoder due to discrepancy between training and inference . |
| Approach: | They propose an optimized training approach to improve dense passage retrieval using RocketQA . they propose cross-batch negatives, denoised hard negatives and data augmentation . |
| Outcome: | The proposed approach outperforms state-of-the-art models on both MSMARCO and Natural Questions. |