---
title: "Who cites Xifeng Yan — 384 papers of our library"
description: "384 papers of the Elamur Research library cite the works of Xifeng Yan, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/xifeng-yan/cited-by/"
---
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# Who cites Xifeng Yan

393 citations of their works from 384 papers of our library (of the 149,439 whose bibliographies we read) · 70 with a page here, the most cited first · [← Xifeng Yan](https://md.research.elamur.ai/free/en/authors/xifeng-yan/)

1. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
2. **[Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/) [IEEE Transactions on Knowledge and Data Engineering](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-knowledge-and-data-engineering/)**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,551 citations*
3. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations*
4. **[TaPas: Weakly Supervised Table Parsing via Pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, et al.*2020 · 847 citations*
5. **[Towards Reasoning in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Huang, Kevin Chen-Chuan Chang*2023 · 780 citations*
6. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models arXiv:2309.12284**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
7. [**AgentBench: Evaluating LLMs as Agents arXiv:2308.03688**Xiao Liu, Hao Yu, Hanchen Zhang, et al.*2023 · 432 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-agentbench-evaluating-llms-agents/)
8. [**A Simple Language Model for Task-Oriented Dialogue arXiv:2005.00796**Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, et al.*2020 · 399 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-asl-2020-simple-language-model-task-oriented/)
9. **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*
10. **[Teaching Small Language Models to Reason](https://md.research.elamur.ai/free/en/papers/magister-2023-teaching-small-language-models-reason/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, et al.*2023 · 343 citations*
11. **[Paraphrase Generation with Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/li-2018-paraphrase-generation-deep-reinforcement-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zichao Li, Xin Jiang, Lifeng Shang, et al.*2018 · 300 citations*
12. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes arXiv:2306.13649**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 299 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/)
13. **[emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://md.research.elamur.ai/free/en/papers/pampari-2018-emrqa-large-corpus-question-answering/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anusri Pampari, Preethi Raghavan, Jennifer Liang, et al.*2018 · 289 citations*
14. [**Measuring Faithfulness in Chain-of-Thought Reasoning arXiv:2307.13702**Tamera Lanham, Anna Chen, Ansh Radhakrishnan, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/lanham-2023-measuring-faithfulness-chain-thought-reasoning/)
15. **[MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems](https://md.research.elamur.ai/free/en/papers/lin-2020-mintl-minimalist-transfer-learning-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, et al.*2020 · 240 citations*
16. **[End-to-End Neural Pipeline for Goal-Oriented Dialogue Systems using GPT-2](https://md.research.elamur.ai/free/en/papers/ham-2020-end-end-neural-pipeline-goal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Donghoon Ham, Jeong-Gwan Lee, Youngsoo Jang, et al.*2020 · 230 citations*
17. [**Specializing Smaller Language Models towards Multi-Step Reasoning arXiv:2301.12726**Yao Fu, Hao Peng, Litu Ou, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-specializing-smaller-language-models-towards/)
18. **[Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 219 citations*
19. **[Finding Microaggressions in the Wild: A Case for Locating Elusive Phenomena in Social Media Posts](https://md.research.elamur.ai/free/en/papers/breitfeller-2019-finding-microaggressions-wild-case-locating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Luke Breitfeller, Emily Ahn, David Jurgens, et al.*2019 · 189 citations*
20. **[Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions](https://md.research.elamur.ai/free/en/papers/zhang-2019-editing-based-sql-query-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rui Zhang, Tao Yu, Heyang Er, et al.*2019 · 177 citations*
21. [**Reasoning with Language Model Prompting: A Survey arXiv:2212.09597**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
22. [**Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction arXiv:2106.06935**Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, et al.*2021 · 159 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2021-neural-bellman-ford-networks-general/)
23. **[SCOTT: Self-Consistent Chain-of-Thought Distillation](https://md.research.elamur.ai/free/en/papers/wang-2023-scott-self-consistent-chain-thought/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peifeng Wang, Zhengyang Wang, Zheng Li, et al.*2023 · 146 citations*
24. **[Symbolic Chain-of-Thought Distillation: Small Models Can Also “Think” Step-by-Step](https://md.research.elamur.ai/free/en/papers/li-2023-symbolic-chain-thought-distillation-small/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liunian Harold Li, Jack Hessel, Youngjae Yu, et al.*2023 · 140 citations*
25. **[NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ximing Lu, Sean Welleck, Peter West, et al.*2022 · 140 citations*
26. [**Text-to-Text Pre-Training for Data-to-Text Tasks arXiv:2005.10433**Mihir Kale, Abhinav Rastogi*2020 · 139 citations*](https://md.research.elamur.ai/free/en/papers/kale-2020-text-text-pre-training-data/)
27. [**DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding arXiv:2412.10302**Zhiyu Wu, Xiaokang Chen, Zizheng Pan, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-deepseek-vl2-mixture-experts/)
28. **[A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://md.research.elamur.ai/free/en/papers/yi-2024-survey-recent-advances-llm-based/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Zihao Yi, Jiarui Ouyang, Zhe Xu, et al.*2024 · 130 citations*
29. [**MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark arXiv:2409.02813**Xiang Yue, Tianyu Zheng, Yuansheng Ni, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mmmu-pro-more-robust-multi/)
30. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
31. **[Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://md.research.elamur.ai/free/en/papers/wu-2024-janus-decoupling-visual-encoding/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Chengyue Wu, Xiaokang Chen, Zhiyu Wu, et al.*2024 · 112 citations*
32. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
33. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
34. **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations*
35. **[Entity Disambiguation with Web Links](https://md.research.elamur.ai/free/en/papers/chisholm-2015-entity-disambiguation-web-links/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Andrew Chisholm, Ben Hachey*2015 · 92 citations*
36. **[Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/paul-2024-making-reasoning-matter-measuring-improving/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Debjit Paul, Robert West, Antoine Bosselut, et al.*2024 · 79 citations*
37. **[Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks](https://md.research.elamur.ai/free/en/papers/kang-2023-knowledge-augmented-reasoning-distillation-small/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Minki Kang, Seanie Lee, Jinheon Baek, et al.*2023 · 71 citations*
38. **[Multi-Domain Dialogue Acts and Response Co-Generation](https://md.research.elamur.ai/free/en/papers/wang-2020-multi-domain-dialogue-acts-response/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Wang, Junfeng Tian, Rui Wang, et al.*2020 · 66 citations*
39. [**Learning From Mistakes Makes LLM Better Reasoner arXiv:2310.20689**Shengnan An, Zexiong Ma, Zeqi Lin, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/an-2023-learning-mistakes-makes-llm-better/)
40. **[Learning an Executable Neural Semantic Parser](https://md.research.elamur.ai/free/en/papers/cheng-2019-learning-executable-neural-semantic-parser/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jianpeng Cheng, Siva Reddy, Vijay Saraswat, et al.*2019 · 63 citations*
41. **[SatLM: Satisfiability-Aided Language Models Using Declarative Prompting](https://md.research.elamur.ai/free/en/papers/ye-2023-satlm-satisfiability-aided-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Qiaochu Chen, Isil Dillig, et al.*2023 · 62 citations*
42. [**Knowledge Card: Filling LLMs' Knowledge Gaps with Plug-in Specialized Language Models arXiv:2305.09955**Shangbin Feng, Weijia Shi, Yuyang Bai, et al.*2023 · 35 citations*](https://md.research.elamur.ai/free/en/papers/feng-2023-knowledge-card-filling-llms-knowledge/)
43. [**The Life Cycle of Knowledge in Big Language Models: A Survey arXiv:2303.07616 · Machine Intelligence Research. vol. 21, no. 2, pp. 217-238, 2024**Boxi Cao, Hongyu Lin, Xianpei Han, et al.*2023 · 31 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-life-cycle-knowledge-big-language/)
44. **[What is the Role of Small Models in the LLM Era: A Survey](https://md.research.elamur.ai/free/en/papers/chen-2024-role-small-models-llm-era/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Lihu Chen, Gael Varoquaux*2024 · 23 citations*
45. **[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*
46. **[The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm Generation](https://md.research.elamur.ai/free/en/papers/loakman-2023-iron-ic-melting-pot-reviewing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tyler Loakman, Aaron Maladry, Chenghua Lin*2023 · 19 citations*
47. [**ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning arXiv:2501.06590**Xiangru Tang, Tianyu Hu, Muyang Ye, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-chemagent-self-updating-library-large/)
48. **[EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction](https://md.research.elamur.ai/free/en/papers/ye-2024-excgec-benchmark-edit-wise-explainable/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Jingheng Ye, Shang Qin, Yinghui Li, et al.*2024 · 15 citations*
49. **[QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-qcrd-quality-guided-contrastive-rationale/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wei Wang, Zhaowei Li, Qi Xu, et al.*2025 · 13 citations*
50. **[SKIntern: Internalizing Symbolic Knowledge for Distilling Better CoT Capabilities into Small Language Models](https://md.research.elamur.ai/free/en/papers/liao-2025-skintern-internalizing-symbolic-knowledge-distilling/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Huanxuan Liao, Shizhu He, Yupu Hao, et al.*2025 · 12 citations*
51. **[SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification](https://md.research.elamur.ai/free/en/papers/wang-2025-sciver-evaluating-foundation-models-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chengye Wang, Yifei Shen, Zexi Kuang, et al.*2025 · 11 citations*
52. [**Survey on English Entity Linking on Wikidata arXiv:2112.01989**Cedric Möller, Jens Lehmann, Ricardo Usbeck*2021 · 10 citations*](https://md.research.elamur.ai/free/en/papers/moller-2021-survey-english-entity-linking-wikidata/)
53. **[A Rose by Any Other Name: LLM-Generated Explanations Are Good Proxies for Human Explanations to Collect Label Distributions on NLI](https://md.research.elamur.ai/free/en/papers/chen-2025-rose-any-other-name-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Beiduo Chen, Siyao Peng, Anna Korhonen, et al.*2025 · 9 citations*
54. **[Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning](https://md.research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Jing Bi, Yuting Wu, Weiwei Xing, et al.*2025 · 8 citations*
55. **[LiTEx: A Linguistic Taxonomy of Explanations for Understanding Within-Label Variation in Natural Language Inference](https://md.research.elamur.ai/free/en/papers/hong-2025-litex-linguistic-taxonomy-explanations-understanding/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Pingjun Hong, Beiduo Chen, Siyao Peng, et al.*2025 · 8 citations*
56. **[TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models](https://md.research.elamur.ai/free/en/papers/he-2025-tablelora-low-rank-adaptation-table/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyi He, Yihao Liu, Mengyu Zhou, et al.*2025 · 8 citations*
57. [**TinyR1-32B-Preview: Boosting Accuracy with Branch-Merge Distillation arXiv:2503.04872**Lin Sun, Guangxiang Zhao, Xiaoqi Jian, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/sun-2025-tinyr1-32b-preview-boosting-accuracy/)
58. [**Large-scale text pre-training helps with dialogue act recognition, but not without fine-tuning**Bill Noble, Vladislav Maraev*2021 · 7 citations*](https://md.research.elamur.ai/free/en/papers/noble-2021-large-scale-text-pre-training/)
59. **[Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution](https://md.research.elamur.ai/free/en/papers/zhou-2026-deputy-accelerating-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhua Zhou, Shichao Weng, Changhai Zhou, et al.*2026 · 1 citation*
60. **[CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data](https://md.research.elamur.ai/free/en/papers/zhang-2026-cyphersmith-transforming-text-cypher-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zeyu Zhang, Kexuan Sun, Zheng Tang, et al.*2026*
61. **[Datasets for Scientific Literature Understanding: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2026-datasets-scientific-literature-understanding-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanzhe Zhang, Xun Zhao, Maodi Hu, et al.*2026*
62. **[EduMARS: Can Vision-Language Models Grade Like Teachers? Benchmarking Multimodal, Rubric-Based Assessment on Chinese K-12 Answers](https://md.research.elamur.ai/free/en/papers/zhao-2026-edumars-can-vision-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuan Zhao, Jiashun Chen, Wanting xu, et al.*2026*
63. **[NSF-CoT: Neuro-Symbolic Formal Verification of Chain-of-Thought Faithfulness in Contextual Question Answering](https://md.research.elamur.ai/free/en/papers/pramanik-2026-nsf-cot-neuro-symbolic-formal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vishal Pramanik, Maisha Maliha, Nathaniel D. Bastian, et al.*2026*
64. **[PolyglotQL: A Pipeline for Multilingual Text-to-SPARQL Dataset Generation](https://md.research.elamur.ai/free/en/papers/perez-2026-polyglotql-pipeline-multilingual-text-sparql/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Julio Perez, Fabio Barth, Georg Rehm*2026*
65. [**Recent Advances in Text-attributed Graph Learning via Large Models (大模型驱动的文本属性图学习研究进展) Acta Automatica Sinica (自动化学报)**Yanping Zheng (郑艳萍), Jiarui Ji (季嘉蕊), Yuwei Hu (胡雨韦), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/)
66. **[Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering](https://md.research.elamur.ai/free/en/papers/tian-2026-subgraph-guided-executable-logical-form/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhang Tian, Dandan Song, Zhijing Wu, et al.*2026*
67. **[Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement](https://md.research.elamur.ai/free/en/papers/shim-2026-which-bird-does-not-have/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Midan Shim, Seokju Hwang, KaeHyun Um, et al.*2026*
68. **[WildSci: Advancing Scientific Reasoning from In-the-Wild Literature](https://md.research.elamur.ai/free/en/papers/liu-2026-wildsci-advancing-scientific-reasoning-wild/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tengxiao Liu, Deepak Nathani, Zekun Li, et al.*2026*
69. **[Survey on Hallucinations in Large Language Models (大语言模型的幻觉问题研究综述)](https://md.research.elamur.ai/free/en/papers/liu-2024-hallucinations-large-language/) [Journal of Software](https://md.research.elamur.ai/free/en/venues/journal-of-software/)**Zeyuan Liu (刘泽垣), Pengjiang Wang (王鹏江), Xiaobin Song (宋晓斌), et al.*2024*
70. **[Barch: an English Dataset of Bar Chart Summaries](https://md.research.elamur.ai/free/en/papers/skrjanec-2022-barch-english-dataset-bar-chart/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Iza Škrjanec, Muhammad Salman Edhi, Vera Demberg*2022*

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