---
title: "Who cites Xiaobao Wu — 146 papers of our library"
description: "146 papers of the Elamur Research library cite the works of Xiaobao Wu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/xiaobao-wu/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/xiaobao-wu/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Xiaobao Wu](https://md.research.elamur.ai/free/en/authors/xiaobao-wu/) / cited by

# Who cites Xiaobao Wu

151 citations of their works from 146 papers of our library (of the 149,439 whose bibliographies we read) · 32 with a page here, the most cited first · [← Xiaobao Wu](https://md.research.elamur.ai/free/en/authors/xiaobao-wu/)

1. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
2. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
3. **[Tool Learning with Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/qu-2024-tool-learning-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Changle Qu, Sunhao Dai, Xiaochi Wei, et al.*2024 · 195 citations*
4. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
5. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/)
6. [**MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback arXiv:2309.10691**Xingyao Wang, Zihan Wang, Jiateng Liu, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mint-evaluating-llms-multi-turn/)
7. [**Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources arXiv:2305.13269**Xingxuan Li, Ruochen Zhao, Yew Ken Chia, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chain-knowledge-grounding-large-language/)
8. **[Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification](https://md.research.elamur.ai/free/en/papers/fadeeva-2024-fact-checking-output-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ekaterina Fadeeva, Aleksandr Rubashevskii, Artem Shelmanov, et al.*2024 · 96 citations*
9. **[Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-self-checker-plug-play-modules/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Miaoran Li, Baolin Peng, Michel Galley, et al.*2024 · 87 citations*
10. [**The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input arXiv:2501.03200**Alon Jacovi, Andrew Wang, Chris Alberti, et al.*2025 · 32 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2025-facts-grounding-leaderboard-benchmarking-llms/)
11. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
12. **[FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/lin-2025-fact-audit-adaptive-multi-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongzhan Lin, Yang Deng, Yuxuan Gu, et al.*2025 · 15 citations*
13. [**SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge arXiv:2509.07968**Lukas Haas, Gal Yona, Giovanni D'Antonio, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/haas-2025-simpleqa-verified-reliable-factuality-benchmark/)
14. **[Piecing It All Together: Verifying Multi-Hop Multimodal Claims](https://md.research.elamur.ai/free/en/papers/wang-2025-piecing-it-all-together-verifying/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Haoran Wang, Aman Rangapur, Xiongxiao Xu, et al.*2025 · 8 citations*
15. **[KG-CRAFT: Knowledge Graph-based Contrastive Reasoning with LLMs for Enhancing Automated Fact-checking](https://md.research.elamur.ai/free/en/papers/lourenco-2026-kg-craft-knowledge-graph-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Vítor Lourenço, Aline Paes, Tillman Weyde, et al.*2026 · 2 citations*
16. **[Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-crowd-llm-augmented-community/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaying Wu, Zihang Fu, Haonan Wang, et al.*2026 · 1 citation*
17. **[From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-passive-metric-active-signal-evolving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaxin Zhang, Wendi Cui, Zhuohang Li, et al.*2026 · 1 citation*
18. **[GAVEL: Evidence-Contract Debate with Mechanized Scrutiny for Provenance-Grounded Fact-Checking](https://md.research.elamur.ai/free/en/papers/xu-2026-gavel-evidence-contract-debate-mechanized/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ruoyu Xu, Gaoxiang Li, Victor S. Sheng*2026 · 1 citation*
19. **[MEVER: Multi-Modal and Explainable Claim Verification with Graph-based Evidence Retrieval](https://md.research.elamur.ai/free/en/papers/zhang-2026-mever-multi-modal-explainable-claim/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Delvin Ce Zhang, Suhan Cui, Zhelin Chu, et al.*2026 · 1 citation*
20. **[ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language Models](https://md.research.elamur.ai/free/en/papers/ni-2026-reprobe-efficient-test-time-scaling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, et al.*2026 · 1 citation*
21. **[Test of Time: Rethinking Temporal Signal of Benchmark Contamination](https://md.research.elamur.ai/free/en/papers/zhang-2026-test-time-rethinking-temporal-signal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Terry Jingchen Zhang, Gopal Dev, Ning Wang, et al.*2026 · 1 citation*
22. **[Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-beyond-examples-automated-thought-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinyang Wu, Mingkuan Feng, Shuai Zhang, et al.*2026*
23. **[Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation](https://md.research.elamur.ai/free/en/papers/wu-2026-curriculum-learning-based-hierarchical-scoring/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiong Wu, Tan Yue, Jianxin Liang, et al.*2026*
24. **[Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection](https://md.research.elamur.ai/free/en/papers/zhang-2026-deconstruct-diagnose-deliberate-protocol-adaptive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhigan Zhang, Jie Sui*2026*
25. **[Evidence Grounding vs. Memorization: Why Neural Semantics Matter for Knowledge Graph Fact Verification](https://md.research.elamur.ai/free/en/papers/upadhyay-2026-evidence-grounding-vs-memorization-why/) [Fact Extraction and VERification Workshop (FEVER)](https://md.research.elamur.ai/free/en/venues/fever/)**Ankit Kumar Upadhyay, John S. Erickson, Deborah L. McGuinness*2026*
26. **[MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive Supervision](https://md.research.elamur.ai/free/en/papers/yang-2026-mard-module-aware-reasoning-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Yang, Jianjun Li, Zhibo Zhang, et al.*2026*
27. **[MAXS: Meta-Adaptive Exploration with LLM Agents](https://md.research.elamur.ai/free/en/papers/zhang-2026-maxs-meta-adaptive-exploration-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jian Zhang, Zhiyuan Wang, Zhangqi Wang, et al.*2026*
28. **[Multi-Constraint State Tracking with Negation: A Diagnostic Benchmark for LLM World Modeling](https://md.research.elamur.ai/free/en/papers/sar-2026-multi-constraint-state-tracking-negation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ayan Sar, Pranav Singh Puri, Sumit Aich, et al.*2026*
29. **[Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework](https://md.research.elamur.ai/free/en/papers/zhang-2026-natural-language-policies-executable-decisions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqiang Zhang, Jing Ma, Zilong Wang, et al.*2026*
30. **[Pushing the Frontiers of Scientific Fact-Checking: The SCINLP Dataset](https://md.research.elamur.ai/free/en/papers/maab-2026-pushing-frontiers-scientific-fact-checking/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Iffat Maab, Junichi Yamagishi*2026*
31. **[ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning](https://md.research.elamur.ai/free/en/papers/smirnov-2026-thinkbooster-unified-framework-seamless-test/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.*2026*
32. **[TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking](https://md.research.elamur.ai/free/en/papers/zhang-2026-trendfact-benchmark-hotspot-perception-automatic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaocheng Zhang, Xi Wang, Yifei Lu, et al.*2026*

114 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/xiaobao-wu/) — Xiaobao Wu