---
title: "Who cites Anirudh Ajith — 370 papers of our library"
description: "370 papers of the Elamur Research library cite the works of Anirudh Ajith, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Anirudh Ajith

371 citations of their works from 370 papers of our library (of the 149,439 whose bibliographies we read) · 116 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Anirudh Ajith](https://md.research.elamur.ai/free/en/authors/anirudh-ajith/)

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1. [**LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code arXiv:2403.07974**Naman Jain, King Han, Alex Gu, et al.*2024 · 578 citations*](https://md.research.elamur.ai/free/en/papers/jain-2024-livecodebench-holistic-contamination-free-evaluation/)
2. [**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/)
3. **[Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 424 citations*
4. **[LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2023-llmlingua-compressing-prompts-accelerated-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al.*2023 · 338 citations*
5. **[Compressing Context to Enhance Inference Efficiency of Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2023-compressing-context-enhance-inference-efficiency/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yucheng Li, Bo Dong, Frank Guerin, et al.*2023 · 212 citations*
6. [**Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers arXiv:2409.04109**Chenglei Si, Diyi Yang, Tatsunori Hashimoto*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/)
7. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations*
8. **[LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 161 citations*
9. [**LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression arXiv:2310.06839**Huiqiang Jiang, Qianhui Wu, Xufang Luo, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-longllmlingua-accelerating-enhancing-llms-long/)
10. [**TOFU: A Task of Fictitious Unlearning for LLMs arXiv:2401.06121**Pratyush Maini, Zhili Feng, Avi Schwarzschild, et al.*2024 · 144 citations*](https://md.research.elamur.ai/free/en/papers/maini-2024-tofu-task-fictitious-unlearning-llms/)
11. [**Agent Laboratory: Using LLM Agents as Research Assistants arXiv:2501.04227**Samuel Schmidgall, Yusheng Su, Ze Wang, et al.*2025 · 139 citations*](https://md.research.elamur.ai/free/en/papers/schmidgall-2025-agent-laboratory-using-llm/)
12. **[Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*
13. [**RAFT: Adapting Language Model to Domain Specific RAG arXiv:2403.10131**Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/)
14. [**Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning arXiv:2404.05868**Ruiqi Zhang, Licong Lin, Yu Bai, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-negative-preference-optimization-catastrophic-collapse/)
15. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)
16. **[LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-lmms-eval-reality-check-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaichen Zhang, Bo Li, Peiyuan Zhang, et al.*2025 · 115 citations*
17. [**RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation arXiv:2310.04408**Fangyuan Xu, Weijia Shi, Eunsol Choi*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/)
18. [**In-context Autoencoder for Context Compression in a Large Language Model arXiv:2307.06945**Tao Ge, Jing Hu, Lei Wang, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-context-autoencoder-context-compression-large/)
19. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/)
20. [**Don't Make Your LLM an Evaluation Benchmark Cheater arXiv:2311.01964**Kun Zhou, Yutao Zhu, Zhipeng Chen, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-don-t-make-llm-evaluation/)
21. [**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/)
22. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations*
23. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/)
24. **[TokenSkip: Controllable Chain-of-Thought Compression in LLMs](https://md.research.elamur.ai/free/en/papers/xia-2025-tokenskip-controllable-chain-thought-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Heming Xia, Chak Tou Leong, Wenjie Wang, et al.*2025 · 106 citations*
25. **[A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://md.research.elamur.ai/free/en/papers/laskar-2024-systematic-survey-critical-review-evaluating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.*2024 · 105 citations*
26. [**Rethinking Benchmark and Contamination for Language Models with Rephrased Samples arXiv:2311.04850**Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/)
27. **[A Careful Examination of Large Language Model Performance on Grade School Arithmetic](https://md.research.elamur.ai/free/en/papers/zhang-2024-careful-examination-large-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugh Zhang, Jeff Da, Dean Lee, et al.*2024 · 101 citations*
28. [**Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security arXiv:2401.05459**Yuanchun Li, Hao Wen, Weijun Wang, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-personal-llm-agents-insights-survey/)
29. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
30. [**LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory arXiv:2410.10813**Di Wu, Hongwei Wang, Wenhao Yu, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-longmemeval-benchmarking-chat-assistants-long/)
31. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 89 citations*
32. **[BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://md.research.elamur.ai/free/en/papers/kuratov-2024-babilong-testing-limits-llms-long/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuri Kuratov, Aydar Bulatov, Petr Anokhin, et al.*2024 · 88 citations*
33. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/)
34. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
35. **[Machine Unlearning of Pre-trained Large Language Models](https://md.research.elamur.ai/free/en/papers/yao-2024-machine-unlearning-pre-trained-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jin Yao, Eli Chien, Minxin Du, et al.*2024 · 86 citations*
36. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations*
37. [**Benchmark Data Contamination of Large Language Models: A Survey arXiv:2406.04244**Cheng Xu, Shuhao Guan, Derek Greene, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/)
38. [**Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention arXiv:2404.07143**Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/munkhdalai-2024-leave-no-context-behind-efficient/)
39. [**SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling arXiv:2312.15166**Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.*2023 · 77 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-solar-10-7b-scaling-large/)
40. [**INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge arXiv:2411.19799**Angelika Romanou, Negar Foroutan, Anna Sotnikova, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/romanou-2024-include-evaluating-multilingual-language-understanding/)
41. [**Benchmarking Benchmark Leakage in Large Language Models arXiv:2404.18824**Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmarking-benchmark-leakage-large-language/)
42. **[Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-alignment-factuality-mitigating-hallucinations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoying Zhang, Baolin Peng, Ye Tian, et al.*2024 · 69 citations*
43. **[C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness](https://md.research.elamur.ai/free/en/papers/kang-2024-c3ot-generating-shorter-chain-thought/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yu Kang, Xianghui Sun, Liangyu Chen, et al.*2024 · 66 citations*
44. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
45. **[mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding](https://md.research.elamur.ai/free/en/papers/hu-2025-mplug-docowl2-high-resolution-compressing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anwen Hu, Haiyang Xu, Liang Zhang, et al.*2025 · 62 citations*
46. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/)
47. **[LightThinker: Thinking Step-by-Step Compression](https://md.research.elamur.ai/free/en/papers/zhang-2025-lightthinker-thinking-step-step-compression/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jintian Zhang, Yuqi Zhu, Mengshu Sun, et al.*2025 · 50 citations*
48. **[HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model](https://md.research.elamur.ai/free/en/papers/hu-2025-hiagent-hierarchical-working-memory-management/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengkang Hu, Tianxing Chen, Qiguang Chen, et al.*2025 · 49 citations*
49. [**Long Context Compression with Activation Beacon arXiv:2401.03462**Peitian Zhang, Zheng Liu, Shitao Xiao, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-long-context-compression-activation-beacon/)
50. **[SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/jia-2024-soul-unlocking-power-second-order/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jinghan Jia, Yihua Zhang, Yimeng Zhang, et al.*2024 · 46 citations*
51. **[Long-Context Language Modeling with Parallel Context Encoding](https://md.research.elamur.ai/free/en/papers/yen-2024-long-context-language-modeling-parallel/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Howard Yen, Tianyu Gao, Danqi Chen*2024 · 44 citations*
52. **[Do Language Models Enjoy Their Own Stories? Prompting Large Language Models for Automatic Story Evaluation](https://md.research.elamur.ai/free/en/papers/chhun-2024-do-language-models-enjoy-their/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Cyril Chhun, Fabian M. Suchanek, Chloé Clavel*2024 · 41 citations*
53. **[CompAct: Compressing Retrieved Documents Actively for Question Answering](https://md.research.elamur.ai/free/en/papers/yoon-2024-compact-compressing-retrieved-documents-actively/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chanwoong Yoon, Taewhoo Lee, Hyeon Hwang, et al.*2024 · 34 citations*
54. **[500xCompressor: Generalized Prompt Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2025-500xcompressor-generalized-prompt-compression-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zongqian Li, Yixuan Su, Nigel Collier*2025 · 32 citations*
55. **[Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/wang-2025-benchmark-self-evolving-multi-agent/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Siyuan Wang, Zhuohan Long, Zhihao Fan, et al.*2025 · 30 citations*
56. [**Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming arXiv:2501.18837**Mrinank Sharma, Meg Tong, Jesse Mu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2025-constitutional-classifiers-defending-against-universal/)
57. [**NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens arXiv:2403.12766**Cunxiang Wang, Ruoxi Ning, Boqi Pan, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-novelqa-benchmarking-question-answering-documents/)
58. [**Sentiment Analysis through LLM Negotiations arXiv:2311.01876**Xiaofei Sun, Xiaoya Li, Shengyu Zhang, et al.*2023 · 25 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-sentiment-analysis-through-llm-negotiations/)
59. **[ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection](https://md.research.elamur.ai/free/en/papers/yan-2026-errorradar-benchmarking-complex-mathematical-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yibo Yan, Shen Wang, Jiahao Huo, et al.*2026 · 23 citations*
60. [**Source-Aware Training Enables Knowledge Attribution in Language Models arXiv:2404.01019**Muhammad Khalifa, David Wadden, Emma Strubell, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/khalifa-2024-source-aware-training-enables-knowledge/)
61. **[Benchmarking LLMs for Translating Classical Chinese Poetry: Evaluating Adequacy, Fluency, and Elegance](https://md.research.elamur.ai/free/en/papers/chen-2025-benchmarking-llms-translating-classical-chinese/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andong Chen, Lianzhang Lou, Kehai Chen, et al.*2025 · 18 citations*
62. **[DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://md.research.elamur.ai/free/en/papers/zhang-2024-darg-dynamic-evaluation-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhehao Zhang, Jiaao Chen, Diyi Yang*2024 · 17 citations*
63. **[Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges](https://md.research.elamur.ai/free/en/papers/samuel-2025-data-contamination-detection-modern-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Vinay Samuel, Yue Zhou, Henry Peng Zou*2025 · 16 citations*
64. **[Attention Entropy is a Key Factor: An Analysis of Parallel Context Encoding with Full-attention-based Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-attention-entropy-is-key-factor/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhisong Zhang, Yan Wang, Xinting Huang, et al.*2025 · 14 citations*
65. **[Learning to Refuse: Towards Mitigating Privacy Risks in LLMs](https://md.research.elamur.ai/free/en/papers/liu-2025-learning-refuse-mitigating-privacy-risks/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhenhua Liu, Tong Zhu, Chuanyuan Tan, et al.*2025 · 13 citations*
66. **[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*
67. **[TripleFact: Defending Data Contamination in the Evaluation of LLM-driven Fake News Detection](https://md.research.elamur.ai/free/en/papers/xu-2025-triplefact-defending-data-contamination-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Xu, Nan Yan*2025 · 12 citations*
68. **[SciArena: An Open Evaluation Platform for Non-Verifiable Scientific Literature-Grounded Tasks](https://md.research.elamur.ai/free/en/papers/zhao-2025-sciarena-open-evaluation-platform-non/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yilun Zhao, Kaiyan Zhang, Tiansheng Hu, et al.*2025 · 11 citations*
69. **[Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models](https://md.research.elamur.ai/free/en/papers/mekala-2025-alternate-preference-optimization-unlearning-factual/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Anmol Mekala, Vineeth Dorna, Shreya Dubey, et al.*2025 · 10 citations*
70. **[GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment](https://md.research.elamur.ai/free/en/papers/tang-2026-gmsa-enhancing-context-compression-group/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiwei Tang, Zhicheng Zhang, Shunlong Wu, et al.*2026 · 8 citations*
71. **[On Many-Shot In-Context Learning for Long-Context Evaluation](https://md.research.elamur.ai/free/en/papers/zou-2025-many-shot-context-learning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kaijian Zou, Muhammad Khalifa, Lu Wang*2025 · 8 citations*
72. [**Associative Recurrent Memory Transformer arXiv:2407.04841**Ivan Rodkin, Yuri Kuratov, Aydar Bulatov, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/rodkin-2024-associative-recurrent-memory-transformer/)
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80. **[When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation](https://md.research.elamur.ai/free/en/papers/tan-2026-when-flores-bloomz-wrong-cross/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**David Tan, Pinzhen Chen, Josef van Genabith, et al.*2026 · 2 citations*
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