---
title: "Who cites Yunlong Liang — 399 papers of our library"
description: "399 papers of the Elamur Research library cite the works of Yunlong Liang, found in their bibliographies; the most cited first."
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---
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# Who cites Yunlong Liang

416 citations of their works from 399 papers of our library (of the 149,439 whose bibliographies we read) · 98 with a page here, the most cited first · [← Yunlong Liang](https://md.research.elamur.ai/free/en/authors/yunlong-liang/)

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. **[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*
3. **[G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment](https://md.research.elamur.ai/free/en/papers/liu-2023-g-eval-nlg-evaluation-gpt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yang Liu, Dan Iter, Yichong Xu, et al.*2023 · 2,000 citations*
4. [**Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models arXiv:2304.01852 · Meta-Radiology (2023)100,017**Yiheng Liu, Tianle Han, Siyuan Ma, et al.*2023 · 940 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-summary-chatgpt-related-research-perspective/)
5. **[Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/) [ACM Transactions on Knowledge Discovery from Data](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-knowledge-discovery-from-data/)**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 814 citations*
6. [**ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate arXiv:2308.07201**Chi-Min Chan, Weize Chen, Yusheng Su, et al.*2023 · 359 citations*](https://md.research.elamur.ai/free/en/papers/chan-2023-chateval-better-llm-based/)
7. [**Ragas: Automated Evaluation of Retrieval Augmented Generation arXiv:2309.15217**Shahul Es, Jithin James, Luis Espinosa-Anke, et al.*2023 · 283 citations*](https://md.research.elamur.ai/free/en/papers/es-2023-ragas-automated-evaluation-retrieval-augmented/)
8. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
9. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques arXiv:2406.06608**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
10. **[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*
11. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
12. **[Learning to Compress Prompts with Gist Tokens](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 178 citations*
13. [**GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts arXiv:2309.10253**Jiahao Yu, Xingwei Lin, Zheng Yu, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-gptfuzzer-red-teaming-large-language/)
14. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization arXiv:2303.15621**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
15. [**Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge arXiv:2410.02736**Jiayi Ye, Yanbo Wang, Yue Huang, et al.*2024 · 148 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-justice-prejudice-quantifying-biases-llm/)
16. [**LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks arXiv:2406.18403**Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.*2024 · 141 citations*](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/)
17. **[InFoBench: Evaluating Instruction Following Ability in Large Language Models](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 136 citations*
18. [**Human-like Summarization Evaluation with ChatGPT arXiv:2304.02554**Mingqi Gao, Jie Ruan, Renliang Sun, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-human-like-summarization-evaluation-chatgpt/)
19. [**Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks arXiv:2306.07899**Veniamin Veselovsky, Manoel Horta Ribeiro, Robert West*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/veselovsky-2023-artificial-artificial-artificial-intelligence-crowd/)
20. **[Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liyan Tang, Tanya Goyal, Alex Fabbri, et al.*2023 · 126 citations*
21. **[Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-models-are-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.*2023 · 119 citations*
22. **[Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study](https://md.research.elamur.ai/free/en/papers/chen-2023-exploring-use-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Chen, Rui Wang, Haiyun Jiang, et al.*2023 · 113 citations*
23. [**BooookScore: A systematic exploration of book-length summarization in the era of LLMs arXiv:2310.00785**Yapei Chang, Kyle Lo, Tanya Goyal, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-booookscore-systematic-exploration-book-length/)
24. **[Benchmarking Foundation Models with Language-Model-as-an-Examiner](https://md.research.elamur.ai/free/en/papers/bai-2023-benchmarking-foundation-models-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yushi Bai, Jiahao Ying, Yixin Cao, et al.*2023 · 106 citations*
25. **[TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models](https://md.research.elamur.ai/free/en/papers/gekhman-2023-trueteacher-learning-factual-consistency-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zorik Gekhman, Jonathan Herzig, Roee Aharoni, et al.*2023 · 106 citations*
26. **[How do Large Language Models Handle Multilingualism?](https://md.research.elamur.ai/free/en/papers/zhao-2024-large-language-models-handle-multilingualism/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiran Zhao, Wenxuan Zhang, Guizhen Chen, et al.*2024 · 102 citations*
27. **[CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation](https://md.research.elamur.ai/free/en/papers/li-2023-coannotating-uncertainty-guided-work-allocation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Minzhi Li, Taiwei Shi, Caleb Ziems, et al.*2023 · 101 citations*
28. **[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*
29. **[FineSurE: Fine-grained Summarization Evaluation using LLMs](https://md.research.elamur.ai/free/en/papers/song-2024-finesure-fine-grained-summarization-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hwanjun Song, Hang Su, Igor Shalyminov, et al.*2024 · 92 citations*
30. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
31. [**Large Language Models are Inconsistent and Biased Evaluators arXiv:2405.01724**Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/stureborg-2024-large-language-models-inconsistent-biased/)
32. [**Safety Assessment of Chinese Large Language Models arXiv:2304.10436**Hao Sun, Zhexin Zhang, Jiawen Deng, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-safety-assessment-chinese-large-language/)
33. **[LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models](https://md.research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adian Liusie, Potsawee Manakul, Mark Gales*2024 · 87 citations*
34. **[Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://md.research.elamur.ai/free/en/papers/raina-2024-is-llm-as-judge-robust/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vyas Raina, Adian Liusie, Mark Gales*2024 · 86 citations*
35. **[Detoxifying Large Language Models via Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-detoxifying-large-language-models-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ningyu Zhang, Ziwen Xu, et al.*2024 · 84 citations*
36. **[Chain-of-Dictionary Prompting Elicits Translation in Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2024-chain-dictionary-prompting-elicits-translation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hongyuan Lu, Haoran Yang, Haoyang Huang, et al.*2024 · 81 citations*
37. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 76 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/)
38. [**Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers arXiv:2404.04925**Libo Qin, Qiguang Chen, Yuhang Zhou, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-multilingual-large-language-model-survey/)
39. [**Wider and Deeper LLM Networks are Fairer LLM Evaluators arXiv:2308.01862**Xinghua Zhang, Bowen Yu, Haiyang Yu, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-wider-deeper-llm-networks-fairer/)
40. [**AlignBench: Benchmarking Chinese Alignment of Large Language Models arXiv:2311.18743**Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-alignbench-benchmarking-chinese-alignment/)
41. **[Unveiling the Implicit Toxicity in Large Language Models](https://md.research.elamur.ai/free/en/papers/wen-2023-unveiling-implicit-toxicity-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Wen, Pei Ke, Hao Sun, et al.*2023 · 72 citations*
42. **[Calibrating LLM-Based Evaluator](https://md.research.elamur.ai/free/en/papers/liu-2024-calibrating-llm-based-evaluator/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yuxuan Liu, Tianchi Yang, Shaohan Huang, et al.*2024 · 71 citations*
43. [**Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? arXiv:2309.07462**Rishav Hada, Varun Gumma, Adrian de Wynter, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/hada-2023-large-language-model-based-evaluators/)
44. [**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/)
45. **[On Context Utilization in Summarization with Large Language Models](https://md.research.elamur.ai/free/en/papers/ravaut-2024-context-utilization-summarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mathieu Ravaut, Aixin Sun, Nancy Chen, et al.*2024 · 67 citations*
46. **[Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://md.research.elamur.ai/free/en/papers/li-2024-leveraging-large-language-models-nlg/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Li, Xiaohan Xu, Tao Shen, et al.*2024 · 63 citations*
47. **[BAMBOO: A Comprehensive Benchmark for Evaluating Long Text Modeling Capacities of Large Language Models](https://md.research.elamur.ai/free/en/papers/dong-2024-bamboo-comprehensive-benchmark-evaluating-long/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Zican Dong, Tianyi Tang, Junyi Li, et al.*2024 · 59 citations*
48. **[The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm](https://md.research.elamur.ai/free/en/papers/aakanksha-2024-multilingual-alignment-prism-aligning-global/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Aakanksha, Arash Ahmadian, Beyza Ermis, et al.*2024 · 49 citations*
49. **[Learning to Edit: Aligning LLMs with Knowledge Editing](https://md.research.elamur.ai/free/en/papers/jiang-2024-learning-edit-aligning-llms-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Chuhan Wu, et al.*2024 · 39 citations*
50. **[Direct Judgement Preference Optimization](https://md.research.elamur.ai/free/en/papers/wang-2025-direct-judgement-preference-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**PeiFeng Wang, Austin Xu, Yilun Zhou, et al.*2025 · 35 citations*
51. **[Retrieval-Augmented Multilingual Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-retrieval-augmented-multilingual-knowledge-editing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weixuan Wang, Barry Haddow, Alexandra Birch*2024 · 34 citations*
52. [**Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation arXiv:2306.05783**Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, et al.*2023 · 34 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-xiezhi-ever-updating-benchmark/)
53. [**BHASA: A Holistic Southeast Asian Linguistic and Cultural Evaluation Suite for Large Language Models arXiv:2309.06085**Wei Qi Leong, Jian Gang Ngui, Yosephine Susanto, et al.*2023 · 33 citations*](https://md.research.elamur.ai/free/en/papers/leong-2023-bhasa-holistic-southeast-asian-linguistic/)
54. **[R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning](https://md.research.elamur.ai/free/en/papers/he-2026-r1-t1-fully-incentivizing-translation/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Minggui He, Yilun Liu, Shimin Tao, et al.*2026 · 22 citations*
55. [**QUDeval: The Evaluation of Questions Under Discussion Discourse Parsing**Yating Wu, Ritika Mangla, Greg Durrett, et al.*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-qudeval-evaluation-questions-under-discussion/)
56. **[MLaKE: Multilingual Knowledge Editing Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/wei-2025-mlake-multilingual-knowledge-editing-benchmark/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zihao Wei, Jingcheng Deng, Liang Pang, et al.*2025 · 20 citations*
57. **[Multilingual Knowledge Editing with Language-Agnostic Factual Neurons](https://md.research.elamur.ai/free/en/papers/zhang-2025-multilingual-knowledge-editing-language-agnostic/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Xue Zhang, Yunlong Liang, Fandong Meng, et al.*2025 · 17 citations*
58. **[Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering](https://md.research.elamur.ai/free/en/papers/sui-2025-can-knowledge-graphs-make-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuan Sui, Yufei He, Zifeng Ding, et al.*2025 · 16 citations*
59. [**Towards Reasoning Ability of Small Language Models arXiv:2502.11569**Gaurav Srivastava, Shuxiang Cao, Xuan Wang*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2025-towards-reasoning-ability-small-language/)
60. **[What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best Practices](https://md.research.elamur.ai/free/en/papers/chen-2025-what-are-essential-factors-crafting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhi Chen, Qiguang Chen, Libo Qin, et al.*2025 · 15 citations*
61. **[Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/zhang-2025-less-but-better-efficient-multilingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xue Zhang, Yunlong Liang, Fandong Meng, et al.*2025 · 13 citations*
62. **[BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning](https://md.research.elamur.ai/free/en/papers/nie-2025-bmike-53-investigating-cross-lingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ercong Nie, Bo Shao, Mingyang Wang, et al.*2025 · 12 citations*
63. [**Aligning Large Language Models with Human Opinions through Persona Selection and Value — Belief — Norm Reasoning arXiv:2311.08385**Do Xuan Long, Kenji Kawaguchi, Min-Yen Kan, et al.*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/long-2023-aligning-large-language-models-human/)
64. **[DeepTrans: Deep Reasoning Translation via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-deeptrans-deep-reasoning-translation-reinforcement/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiaan Wang, Fandong Meng, Jie Zhou*2026 · 11 citations*
65. **[Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/zhang-2026-think-natively-unlocking-multilingual-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xue Zhang, Yunlong Liang, Fandong Meng, et al.*2026 · 11 citations*
66. **[SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation](https://md.research.elamur.ai/free/en/papers/yang-2026-ssr-zero-simple-self-rewarding/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenjie Yang, Mao Zheng, Mingyang Song, et al.*2026 · 10 citations*
67. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
68. **[Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study](https://md.research.elamur.ai/free/en/papers/song-2025-can-many-shot-context-learning/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyang Song, Mao Zheng, Xuan Luo*2025 · 9 citations*
69. **[Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks](https://md.research.elamur.ai/free/en/papers/li-2025-exploring-reliability-large-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Qintong Li, Leyang Cui, Lingpeng Kong, et al.*2025 · 7 citations*
70. **[Beyond Literal Mapping: Benchmarking and Improving Non-Literal Translation Evaluation](https://md.research.elamur.ai/free/en/papers/tian-2026-beyond-literal-mapping-benchmarking-improving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanzhi Tian, Cunxiang Wang, Zeming Liu, et al.*2026 · 5 citations*
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