---
title: "Who cites Tianxing He — 319 papers of our library"
description: "319 papers of the Elamur Research library cite the works of Tianxing He, found in their bibliographies; the most cited first."
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---
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# Who cites Tianxing He

332 citations of their works from 319 papers of our library (of the 149,439 whose bibliographies we read) · 75 with a page here, the most cited first · [← Tianxing He](https://md.research.elamur.ai/free/en/authors/tianxing-he/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. **[Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,214 citations*
3. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 700 citations*
4. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
5. [**Neural Text Generation with Unlikelihood Training arXiv:1908.04319**Sean Welleck, Ilia Kulikov, Stephen Roller, et al.*2019 · 513 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/)
6. **[Graph Retrieval-Augmented Generation: A Survey](https://md.research.elamur.ai/free/en/papers/peng-2024-graph-retrieval-augmented-generation-survey/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Boci Peng, Yun Zhu, Yongchao Liu, et al.*2024 · 276 citations*
7. **[CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shuyang Cao, Lu Wang*2021 · 260 citations*
8. **[G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://md.research.elamur.ai/free/en/papers/he-2024-g-retriever-retrieval-augmented-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiaoxin He, Yijun Tian, Yifei Sun, et al.*2024 · 236 citations*
9. **[Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Margaret Li, Stephen Roller, Ilia Kulikov, et al.*2020 · 217 citations*
10. **[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*
11. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
12. [**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/)
13. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
14. **[Locally Typical Sampling](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Clara Meister, Tiago Pimentel, Gian Wiher, et al.*2023 · 157 citations*
15. **[LLM-based NLG Evaluation: Current Status and Challenges](https://md.research.elamur.ai/free/en/papers/gao-2025-llm-based-nlg-evaluation-current/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Mingqi Gao, Xinyu Hu, Xunjian Yin, et al.*2025 · 153 citations*
16. **[Can Language Models Solve Graph Problems in Natural Language?](https://md.research.elamur.ai/free/en/papers/wang-2023-language-models-solve-graph-problems/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Heng Wang, Shangbin Feng, Tianxing He, et al.*2023 · 124 citations*
17. [**Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews arXiv:2403.07183**Weixin Liang, Zachary Izzo, Yaohui Zhang, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liang-2024-monitoring-ai-modified-content/)
18. **[Truncation Sampling as Language Model Desmoothing](https://md.research.elamur.ai/free/en/papers/hewitt-2022-truncation-sampling-as-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Hewitt, Christopher Manning, Percy Liang*2022 · 95 citations*
19. [**Retrieval-Augmented Generation with Graphs (GraphRAG) arXiv:2501.00309**Haoyu Han, Yu Wang, Harry Shomer, et al.*2025 · 87 citations*](https://md.research.elamur.ai/free/en/papers/han-2025-retrieval-augmented-generation-graphs-graphrag/)
20. [**Talk like a Graph: Encoding Graphs for Large Language Models arXiv:2310.04560**Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/fatemi-2023-talk-like-graph-encoding-graphs/)
21. **[On Decoding Strategies for Neural Text Generators](https://md.research.elamur.ai/free/en/papers/wiher-2022-decoding-strategies-neural-text-generators/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Gian Wiher, Clara Meister, Ryan Cotterell*2022 · 81 citations*
22. [**Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning arXiv:2305.19523**Xiaoxin He, Xavier Bresson, Thomas Laurent, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-harnessing-explanations-llm-lm-interpreter/)
23. **[MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://md.research.elamur.ai/free/en/papers/li-2024-mediq-question-asking-llms-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, et al.*2024 · 74 citations*
24. [**When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards arXiv:2402.01781**Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/alzahrani-2024-benchmarks-targets-revealing-sensitivity-large/)
25. **[Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-tug-war-between-knowledge-exploring/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Zhuoran Jin, Pengfei Cao, Yubo Chen, et al.*2024 · 70 citations*
26. [**CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation arXiv:2311.18702**Pei Ke, Bosi Wen, Zhuoer Feng, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/ke-2023-critiquellm-informative-critique-generation/)
27. **[Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration](https://md.research.elamur.ai/free/en/papers/feng-2024-modular-pluralism-pluralistic-alignment-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shangbin Feng, Taylor Sorensen, Yuhan Liu, et al.*2024 · 68 citations*
28. [**In-context Pretraining: Language Modeling Beyond Document Boundaries arXiv:2310.10638**Weijia Shi, Sewon Min, Maria Lomeli, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-context-pretraining-language-modeling-document/)
29. **[CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2024-clamber-benchmark-identifying-clarifying-ambiguous/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tong Zhang, Peixin Qin, Yang Deng, et al.*2024 · 52 citations*
30. [**Resolving Knowledge Conflicts in Large Language Models arXiv:2310.00935**Yike Wang, Shangbin Feng, Heng Wang, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-resolving-knowledge-conflicts-large-language/)
31. **[Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts?](https://md.research.elamur.ai/free/en/papers/tan-2024-blinded-generated-contexts-how-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hexiang Tan, Fei Sun, Wanli Yang, et al.*2024 · 37 citations*
32. [**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/)
33. **[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*
34. **[When One LLM Drools, Multi-LLM Collaboration Rules](https://md.research.elamur.ai/free/en/papers/feng-2026-when-one-llm-drools-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shangbin Feng, Wenxuan Ding, Alisa Liu, et al.*2026 · 14 citations*
35. **[Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective](https://md.research.elamur.ai/free/en/papers/zeng-2025-knowledge-checking-retrieval-augmented-generation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Shenglai Zeng, Jiankun Zhang, Bingheng Li, et al.*2025 · 14 citations*
36. **[Analyzing Multilingual Competency of LLMs in Multi-Turn Instruction Following: A Case Study of Arabic](https://md.research.elamur.ai/free/en/papers/boughorbel-2023-analyzing-multilingual-competency-llms-multi/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Sabri Boughorbel, Majd Hawasly*2023 · 14 citations*
37. **[Can LLM Watermarks Robustly Prevent Unauthorized Knowledge Distillation?](https://md.research.elamur.ai/free/en/papers/pan-2025-can-llm-watermarks-robustly-prevent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leyi Pan, Aiwei Liu, Shiyu Huang, et al.*2025 · 11 citations*
38. [**Understanding Finetuning for Factual Knowledge Extraction from Language Models arXiv:2301.11293**Mehran Kazemi, Sid Mittal, Deepak Ramachandran*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/kazemi-2023-understanding-finetuning-factual-knowledge-extraction/)
39. **[Beyond Math: Stories as a Testbed for Memorization-Constrained Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/jiang-2026-beyond-math-stories-as-testbed/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yuxuan Jiang, Francis Ferraro*2026 · 10 citations*
40. **[Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring](https://md.research.elamur.ai/free/en/papers/hua-2026-rethinking-jailbreak-detection-large-vision/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peichun Hua, Hao Li, Shanghao Shi, et al.*2026 · 10 citations*
41. **[Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?](https://md.research.elamur.ai/free/en/papers/ge-2025-can-graph-descriptive-order-affect/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuyao Ge, Shenghua Liu, Baolong Bi, et al.*2025 · 10 citations*
42. **[GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/yuan-2025-gracore-benchmarking-graph-comprehension-complex/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zike Yuan, Ming Liu, Hui Wang, et al.*2025 · 9 citations*
43. **[Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://md.research.elamur.ai/free/en/papers/zeng-2025-context-robust-llms-gated-representation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shenglai Zeng, Pengfei He, Kai Guo, et al.*2025 · 5 citations*
44. **[Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection](https://md.research.elamur.ai/free/en/papers/zhang-2026-bidirectional-lms-are-better-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuwei Zhang, Wenhao Yu, Shangbin Feng, et al.*2026 · 4 citations*
45. **[Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs](https://md.research.elamur.ai/free/en/papers/zhang-2026-do-images-speak-louder-than/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chi Zhang, Wenxuan Ding, Jiale Liu, et al.*2026 · 2 citations*
46. **[Position: LLM Watermarking Should Align Stakeholders' Incentives for Practical Adoption](https://md.research.elamur.ai/free/en/papers/liu-2026-position-llm-watermarking-should-align/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yepeng Liu, Xuandong Zhao, Dawn Song, et al.*2026 · 2 citations*
47. **[Agent-based Substructure Counting under Local Differential Privacy](https://md.research.elamur.ai/free/en/papers/zhang-2026-agent-based-substructure-counting-under/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuting Zhang, Kai Wang, Wei Ni, et al.*2026 · 1 citation*
48. **[Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems](https://md.research.elamur.ai/free/en/papers/yang-2026-among-us-measuring-mitigating-malicious/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyuan Yang, Wenxuan Ding, Shangbin Feng, et al.*2026 · 1 citation*
49. **[Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation](https://md.research.elamur.ai/free/en/papers/wang-2026-exploring-graph-learning-tasks-pure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxiang Wang, Xinnan Dai, Wenqi Fan, et al.*2026 · 1 citation*
50. **[From Attack Surfaces to Actual Operations: A Survey of Modern LLM Jailbreaks](https://md.research.elamur.ai/free/en/papers/zhou-2026-attack-surfaces-actual-operations-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ruikang Zhou, Changsheng Sun, Mark Huasong Meng*2026 · 1 citation*
51. **[Measuring Human Contribution in AI-Assisted Content Generation](https://md.research.elamur.ai/free/en/papers/xie-2026-measuring-human-contribution-ai-assisted/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yueqi Xie, Tao Qi, Jingwei Yi, et al.*2026 · 1 citation*
52. **[Topic-Based Watermarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/nemecek-2026-topic-based-watermarks-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexander Nemecek, Yuzhou Jiang, Erman Ayday*2026 · 1 citation*
53. **[USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS](https://md.research.elamur.ai/free/en/papers/zheng-2026-usb-comprehensive-unified-safety-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Baolin Zheng, Guanlin Chen, Qingyang Teng, et al.*2026 · 1 citation*
54. **[Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction](https://md.research.elamur.ai/free/en/papers/pei-2026-analyze-like-venture-capitalist-information/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, et al.*2026*
55. **[Asking language models how to represent data for fine-tuning](https://md.research.elamur.ai/free/en/papers/singh-2026-asking-language-models-how-represent/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Usneek Singh, Ananya Singha, Abhijeet Awasthi, et al.*2026*
56. **[Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models](https://md.research.elamur.ai/free/en/papers/zangari-2026-colorful-talks-graphs-human-interpretable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Angelo Zangari, Peyman Baghershahi, Sourav Medya*2026*
57. **[Corpus and Baselines for Distinguishing Authentic, AI-Generated, and AI-Enhanced Resumes](https://md.research.elamur.ai/free/en/papers/loizidou-2026-corpus-baselines-distinguishing-authentic-ai/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Andrea Loizidou, Anshu Kiran Sharma, Adrian Esquivel, et al.*2026*
58. **[DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs](https://md.research.elamur.ai/free/en/papers/xu-2026-dmn-compositional-framework-jailbreaking-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenzhuo Xu, Zhipeng Wei, Zonghao Ying, et al.*2026*
59. **[EntroBench: Evaluating LLM Watermarking Under Multi-Entropy Scenarios and Practical User Operations](https://md.research.elamur.ai/free/en/papers/qin-2026-entrobench-evaluating-llm-watermarking-under/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pengyuan Qin, Linnan Tu, Yuhan Ke, et al.*2026*
60. **[EVA: Evolving Semantic Adversaries for Red-Teaming GUI Agents Against Environmental Injection Attacks](https://md.research.elamur.ai/free/en/papers/lu-2026-eva-evolving-semantic-adversaries-red/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yijie Lu, Manman Zhao, Tianjie Ju, et al.*2026*
61. **[Faithfulness vs. Safety: Evaluating LLM Behavior Under Counterfactual Medical Evidence](https://md.research.elamur.ai/free/en/papers/mo-2026-faithfulness-vs-safety-evaluating-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaijie Mo, Siddhartha Venkatayogi, Chantal Shaib, et al.*2026*
62. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026*
63. **[Generalization in Graph Reasoning: A Systematic Comparison of LLM Training Approaches](https://md.research.elamur.ai/free/en/papers/shirai-2026-generalization-graph-reasoning-systematic-comparison/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Sola Shirai, Kavitha Srinivas, Julian Dolby, et al.*2026*
64. **[How to Make LMs Strong Node Classifiers?](https://md.research.elamur.ai/free/en/papers/xu-2026-how-make-lms-strong-node/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhe Xu, Kaveh Hassani, Si Zhang, et al.*2026*
65. **[MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models](https://md.research.elamur.ai/free/en/papers/makwana-2026-mlingualfc-evaluating-jailbreak-vulnerabilities-multilingual/) [Workshop on Multilinguality in the Era of Large Language Models](https://md.research.elamur.ai/free/en/venues/mellm/)**Rishabh Makwana, Mamta Mamta, Deeksha Varshney, et al.*2026*
66. **[MuSe: Multi-Stage Graph Reasoning via Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-muse-multi-stage-graph-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanyu Wang, Xu Chu, Zhijie Tan, et al.*2026*
67. **[NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering](https://md.research.elamur.ai/free/en/papers/shi-2026-ng-router-graph-supervised-multi/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, et al.*2026*
68. **[Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation](https://md.research.elamur.ai/free/en/papers/ma-2026-private-seeds-public-llms-realistic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Ma, Sarah Rajtmajer*2026*
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