---
title: "Who cites Rameswar Panda — 522 papers of our library"
description: "522 papers of the Elamur Research library cite the works of Rameswar Panda, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Rameswar Panda

539 citations of their works from 522 papers of our library (of the 149,439 whose bibliographies we read) · 118 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Rameswar Panda](https://md.research.elamur.ai/free/en/authors/rameswar-panda/)

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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. [**Yi: Open Foundation Models by 01.AI arXiv:2403.04652**Alex Young, Bei Chen, Chao Li, et al.*2024 · 408 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
3. [**RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/)
4. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference arXiv:2412.13663**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 358 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/)
5. **[xLSTM: Extended Long Short-Term Memory](https://md.research.elamur.ai/free/en/papers/beck-2024-xlstm-extended-long-short-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.*2024 · 296 citations*
6. **[HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://md.research.elamur.ai/free/en/papers/gutierrez-2024-hipporag-neurobiologically-inspired-long-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, et al.*2024 · 268 citations*
7. **[MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://md.research.elamur.ai/free/en/papers/jiang-2024-minference-1-0-accelerating-pre/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, et al.*2024 · 202 citations*
8. [**FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving MLSys 2025**Zihao Ye, Lequn Chen, Ruihang Lai, et al.*2025 · 177 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-flashinfer-efficient-customizable-attention-engine/)
9. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions arXiv:2406.15877**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
10. **[The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations](https://md.research.elamur.ai/free/en/papers/rawte-2023-troubling-emergence-hallucination-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, et al.*2023 · 171 citations*
11. [**PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling arXiv:2406.02069**Zefan Cai, Yichi Zhang, Bofei Gao, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-pyramidkv-dynamic-kv-cache-compression/)
12. **[ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings](https://md.research.elamur.ai/free/en/papers/hao-2023-toolkengpt-augmenting-frozen-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shibo Hao, Tianyang Liu, Zhen Wang, et al.*2023 · 147 citations*
13. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations*
14. **[MLVU: Benchmarking Multi-task Long Video Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2024-mlvu-benchmarking-multi-task-long/) [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/)**Junjie Zhou, Yan Shu, Bo Zhao, et al.*2024 · 140 citations*
15. [**Long-context LLMs Struggle with Long In-context Learning arXiv:2404.02060**Tianle Li, Ge Zhang, Quy Duc Do, et al.*2024 · 138 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-long-context-llms-struggle-long/)
16. [**LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks arXiv:2412.15204**Yushi Bai, Shangqing Tu, Jiajie Zhang, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longbench-v2-deeper-understanding/)
17. [**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/)
18. [**Data Engineering for Scaling Language Models to 128K Context arXiv:2402.10171**Yao Fu, Rameswar Panda, Xinyao Niu, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-data-engineering-scaling-language-models/)
19. [**PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization arXiv:2310.16427**Xinyuan Wang, Chenxi Li, Zhen Wang, et al.*2023 · 102 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-promptagent-strategic-planning-language-models/)
20. **[Make Your LLM Fully Utilize the Context](https://md.research.elamur.ai/free/en/papers/an-2024-make-llm-fully-utilize-context/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shengnan An, Zexiong Ma, Zeqi Lin, et al.*2024 · 97 citations*
21. [**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/)
22. [**Retrieval Head Mechanistically Explains Long-Context Factuality arXiv:2404.15574**Wenhao Wu, Yizhong Wang, Guangxuan Xiao, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-head-mechanistically-explains-long/)
23. **[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*
24. [**PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models arXiv:2404.02948**Fanxu Meng, Zhaohui Wang, Muhan Zhang*2024 · 86 citations*](https://md.research.elamur.ai/free/en/papers/meng-2024-pissa-principal-singular-values-singular/)
25. **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 81 citations*
26. [**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/)
27. [**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/)
28. [**Long Context Transfer from Language to Vision arXiv:2406.16852**Peiyuan Zhang, Kaichen Zhang, Bo Li, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-long-context-transfer-language-vision/)
29. [**MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention arXiv:2506.13585**MiniMax, Aili Chen, Aonian Li, et al.*2025 · 66 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-m1-scaling-test/)
30. [**LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs arXiv:2408.07055**Yushi Bai, Jiajie Zhang, Xin Lv, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longwriter-unleashing-10-000/)
31. [**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/)
32. [**Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling arXiv:2406.07522**Liliang Ren, Yang Liu, Yadong Lu, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/ren-2024-samba-simple-hybrid-state-space/)
33. **[The Mamba in the Llama: Distilling and Accelerating Hybrid Models](https://md.research.elamur.ai/free/en/papers/wang-2024-mamba-llama-distilling-accelerating-hybrid/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Junxiong Wang; Daniele Paliotta; Avner May; Alexander M. Rush;, Tri Dao*2024 · 59 citations*
34. **[Reducing Transformer Key-Value Cache Size with Cross-Layer Attention](https://md.research.elamur.ai/free/en/papers/brandon-2024-reducing-transformer-key-value-cache/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**William Brandon, Mayank Mishra, Aniruddha Nrusimha, et al.*2024 · 57 citations*
35. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
36. [**HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly arXiv:2410.02694**Howard Yen, Tianyu Gao, Minmin Hou, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/yen-2024-helmet-evaluate-long-context-language/)
37. [**A Survey of Resource-efficient LLM and Multimodal Foundation Models arXiv:2401.08092**Mengwei Xu, Wangsong Yin, Dongqi Cai, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-survey-resource-efficient-llm-multimodal/)
38. [**Kimi Linear: An Expressive, Efficient Attention Architecture arXiv:2510.26692**Kimi Team, Yu Zhang, Zongyu Lin, et al.*2025 · 51 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-linear-expressive-efficient/)
39. [**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/)
40. [**Hymba: A Hybrid-head Architecture for Small Language Models arXiv:2411.13676**Xin Dong, Yonggan Fu, Shizhe Diao, et al.*2024 · 50 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-hymba-hybrid-head-architecture-small/)
41. [**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/)
42. [**Stable LM 2 1.6B Technical Report arXiv:2402.17834**Marco Bellagente, Jonathan Tow, Dakota Mahan, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/bellagente-2024-stable-lm-2-1-6b/)
43. **[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*
44. **[SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios](https://md.research.elamur.ai/free/en/papers/orel-2026-semeval-2026-task-13-detecting/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Daniil Orel, Dilshod Azizov, Indraneil Paul, et al.*2026 · 41 citations*
45. [**DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models arXiv:2507.09955**Luolin Xiong, Haofen Wang, Xi Chen, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2025-deepseek-paradigm-shifts-technical/)
46. [**Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks arXiv:2402.04248**Jongho Park, Jaeseung Park, Zheyang Xiong, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-mamba-learn-learn-comparative-study/)
47. **[LongEmbed: Extending Embedding Models for Long Context Retrieval](https://md.research.elamur.ai/free/en/papers/zhu-2024-longembed-extending-embedding-models-long/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dawei Zhu, Liang Wang, Nan Yang, et al.*2024 · 37 citations*
48. [**MambaByte: Token-free Selective State Space Model arXiv:2401.13660**Junxiong Wang, Tushaar Gangavarapu, Jing Nathan Yan, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-mambabyte-token-free-selective-state/)
49. [**Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models arXiv:2401.04658**Zhen Qin, Weigao Sun, Dong Li, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-lightning-attention-2-free/)
50. [**Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization arXiv:2405.15071**Boshi Wang, Xiang Yue, Yu Su, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-grokked-transformers-implicit-reasoners-mechanistic/)
51. [**No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models arXiv:2307.06440**Jean Kaddour, Oscar Key, Piotr Nawrot, et al.*2023 · 30 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-no-train-no-gain-revisiting/)
52. [**JetMoE: Reaching Llama2 Performance with 0.1M Dollars arXiv:2404.07413**Yikang Shen, Zhen Guo, Tianle Cai, et al.*2024 · 28 citations*](https://md.research.elamur.ai/free/en/papers/shen-2024-jetmoe-reaching-llama2-performance-0/)
53. [**Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness arXiv:2309.11064**Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, et al.*2023 · 27 citations*](https://md.research.elamur.ai/free/en/papers/rawte-2023-exploring-relationship-between-llm-hallucinations/)
54. [**Step-3 is Large yet Affordable: Model-system Co-design for Cost-effective Decoding arXiv:2507.19427**StepFun, Bin Wang, Bojun Wang, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/stepfun-2025-step-3-large-yet/)
55. [**Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent arXiv:2411.02265**Xingwu Sun, Yanfeng Chen, Yiqing Huang, et al.*2024 · 25 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-hunyuan-large-open-source/)
56. **[AICD Bench: A Challenging Benchmark for AI-Generated Code Detection](https://md.research.elamur.ai/free/en/papers/orel-2026-aicd-bench-challenging-benchmark-ai/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Daniil Orel, Dilshod Azizov, Indraneil Paul, et al.*2026 · 23 citations*
57. [**RWKV-7 "Goose" with Expressive Dynamic State Evolution arXiv:2503.14456**Bo Peng, Ruichong Zhang, Daniel Goldstein, et al.*2025 · 23 citations*](https://md.research.elamur.ai/free/en/papers/peng-2025-rwkv-7-goose-expressive-dynamic/)
58. **[Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-multimodal-needle-haystack-benchmarking-long/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hengyi Wang, Haizhou Shi, Shiwei Tan, et al.*2025 · 21 citations*
59. [**Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models arXiv:2401.00788**Terry Yue Zhuo, Armel Zebaze, Nitchakarn Suppattarachai, et al.*2024 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-astraios-parameter-efficient-instruction-tuning/)
60. **[Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models](https://md.research.elamur.ai/free/en/papers/song-2025-counting-stars-multi-evidence-position/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyang Song, Mao Zheng, Xuan Luo*2025 · 20 citations*
61. **[LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models](https://md.research.elamur.ai/free/en/papers/hu-2025-longrecipe-recipe-efficient-long-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiyuan Hu, Yuliang Liu, Jinman Zhao, et al.*2025 · 16 citations*
62. **[NESTFUL: A Benchmark for Evaluating LLMs on Nested Sequences of API Calls](https://md.research.elamur.ai/free/en/papers/basu-2025-nestful-benchmark-evaluating-llms-nested/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kinjal Basu, Ibrahim Abdelaziz, Kiran Kate, et al.*2025 · 16 citations*
63. **[Arctic-Text2SQL-R1: Simple Rewards, Strong Reasoning in Text-to-SQL](https://md.research.elamur.ai/free/en/papers/yao-2026-arctic-text2sql-r1-simple-rewards/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhewei Yao, Guoheng Sun, Łukasz Borchmann, et al.*2026 · 15 citations*
64. [**A Survey on Latent Reasoning arXiv:2507.06203**Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-survey-latent-reasoning/)
65. **[L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models](https://md.research.elamur.ai/free/en/papers/jeon-2025-l4q-parameter-efficient-quantization-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hyesung Jeon, Yulhwa Kim, Jae-Joon Kim*2025 · 15 citations*
66. **[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*
67. **[Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation](https://md.research.elamur.ai/free/en/papers/guo-2023-bridging-gap-between-synthetic-authentic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenyu Guo, Qingkai Fang, Dong Yu, et al.*2023 · 15 citations*
68. [**QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning arXiv:2505.17667**Fanqi Wan, Weizhou Shen, Shengyi Liao, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/wan-2025-qwenlong-l1-long-context/)
69. **[LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs](https://md.research.elamur.ai/free/en/papers/chen-2025-ladm-long-context-training-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianghao Chen, Junhong Wu, Yangyifan Xu, et al.*2025 · 12 citations*
70. [**Linear Attention Sequence Parallelism arXiv:2404.02882**Weigao Sun, Zhen Qin, Dong Li, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/sun-2024-linear-attention-sequence-parallelism/)
71. **[Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric](https://md.research.elamur.ai/free/en/papers/yang-2025-measuring-data-diversity-instruction-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Yang Nan, Junjie Ye, et al.*2025 · 11 citations*
72. **[B'MOJO: Hybrid State Space Realizations of Foundation Models with Eidetic and Fading Memory](https://md.research.elamur.ai/free/en/papers/zancato-2024-b-mojo-hybrid-state-space/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Luca Zancato, Arjun Seshadri, Yonatan Dukler, et al.*2024 · 11 citations*
73. [**EventRL: Enhancing Event Extraction with Outcome Supervision for Large Language Models arXiv:2402.11430**Jun Gao, Huan Zhao, Wei Wang, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-eventrl-enhancing-event-extraction-outcome/)
74. [**Longhorn: State Space Models are Amortized Online Learners arXiv:2407.14207**Bo Liu, Rui Wang, Lemeng Wu, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-longhorn-state-space-models-amortized/)
75. [**Yi-Lightning Technical Report arXiv:2412.01253**Alan Wake, Bei Chen, C.X. Lv, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/wake-2024-yi-lightning-technical-report/)
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