---
title: "Who cites Sadhika Malladi — 459 papers of our library"
description: "459 papers of the Elamur Research library cite the works of Sadhika Malladi, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Sadhika Malladi

470 citations of their works from 459 papers of our library (of the 149,439 whose bibliographies we read) · 110 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Sadhika Malladi](https://md.research.elamur.ai/free/en/authors/sadhika-malladi/)

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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. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
3. [**Qwen3-VL Technical Report arXiv:2511.21631**Shuai Bai, Yuxuan Cai, Ruizhe Chen, et al.*2025 · 370 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/)
4. [**Retrieval-Augmented Generation for AI-Generated Content: A Survey arXiv:2402.19473**Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/)
5. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
6. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
7. [**OpenAI GPT-5 System Card arXiv:2601.03267**Aaditya Singh, Adam Fry, Adam Perelman, et al.*2025 · 279 citations*](https://md.research.elamur.ai/free/en/papers/singh-2025-openai-gpt-5-system-card/)
8. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
9. [**InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models arXiv:2504.10479**Jinguo Zhu, Weiyun Wang, Zhe Chen, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)
10. **[ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 200 citations*
11. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency arXiv:2508.18265**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/)
12. [**LESS: Selecting Influential Data for Targeted Instruction Tuning arXiv:2402.04333**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 178 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/)
13. **[Fine-Tuning Language Models with Just Forward Passes](https://md.research.elamur.ai/free/en/papers/malladi-2023-fine-tuning-language-models-just/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Sadhika Malladi, Tianyu Gao, Eshaan Nichani, et al.*2023 · 153 citations*
14. **[Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://md.research.elamur.ai/free/en/papers/lv-2024-full-parameter-fine-tuning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kai Lv, Yuqing Yang, Tengxiao Liu, et al.*2024 · 143 citations*
15. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
16. [**Fanar: An Arabic-Centric Multimodal Generative AI Platform arXiv:2501.13944**Fanar Team: Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, et al.*2025 · 127 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/)
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. [**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/)
19. **[Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://md.research.elamur.ai/free/en/papers/wang-2025-reinforcement-learning-reasoning-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.*2025 · 87 citations*
20. [**DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving arXiv:2407.13690**Yuxuan Tong, Xiwen Zhang, Rui Wang, et al.*2024 · 81 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-dart-math-difficulty-aware-rejection/)
21. **[A Thorough Examination of Decoding Methods in the Era of LLMs](https://md.research.elamur.ai/free/en/papers/shi-2024-thorough-examination-decoding-methods-era/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chufan Shi, Haoran Yang, Deng Cai, et al.*2024 · 68 citations*
22. [**Seed1.5-VL Technical Report arXiv:2505.07062**Dong Guo, Faming Wu, Feida Zhu, et al.*2025 · 67 citations*](https://md.research.elamur.ai/free/en/papers/guo-2025-seed1-5-vl-technical/)
23. [**Kimi K2.5: Visual Agentic Intelligence arXiv:2602.02276**Kimi Team, Tongtong Bai, Yifan Bai, et al.*2026 · 66 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2026-kimi-k2-5-visual/)
24. [**Rho-1: Not All Tokens Are What You Need arXiv:2404.07965**Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/)
25. [**RegMix: Data Mixture as Regression for Language Model Pre-training arXiv:2407.01492**Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-regmix-data-mixture-regression-language/)
26. [**MiMo-VL Technical Report arXiv:2506.03569**Xiaomi LLM-Core Team, Zihao Yue, Zhenru Lin, et al.*2025 · 33 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-llm-mimo-vl-technical-report/)
27. [**Qwen3.5-Omni Technical Report arXiv:2604.15804**Qwen Team*2026 · 29 citations*](https://md.research.elamur.ai/free/en/papers/qwen-team-2026-qwen3-5-omni-technical/)
28. **[Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://md.research.elamur.ai/free/en/papers/eger-2025-transforming-science-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Steffen Eger, Yong Cao, Jennifer D'Souza, et al.*2025 · 25 citations*
29. **[ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation](https://md.research.elamur.ai/free/en/papers/zhao-2025-chartcoder-advancing-multimodal-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuanle Zhao, Xianzhen Luo, Qi Shi, et al.*2025 · 24 citations*
30. **[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*
31. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
32. [**MMSci: A Dataset for Graduate-Level Multi-Discipline Multimodal Scientific Understanding arXiv:2407.04903**Zekun Li, Xianjun Yang, Kyuri Choi, et al.*2024 · 20 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-mmsci-dataset-graduate-level-multi/)
33. **[Suri: Multi-constraint Instruction Following in Long-form Text Generation](https://md.research.elamur.ai/free/en/papers/pham-2024-suri-multi-constraint-instruction-following/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chau Minh Pham, Simeng Sun, Mohit Iyyer*2024 · 19 citations*
34. **[A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://md.research.elamur.ai/free/en/papers/luo-2025-survey-efficient-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyu Luo, Bohan Wu, Xiao Luo, et al.*2025 · 14 citations*
35. **[Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering](https://md.research.elamur.ai/free/en/papers/si-2025-aligning-large-language-models-follow/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuzheng Si, Haozhe Zhao, Gang Chen, et al.*2025 · 11 citations*
36. **[Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment](https://md.research.elamur.ai/free/en/papers/li-2025-layer-aware-representation-filtering-purifying/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Li, Lijun Li, Zhenghao Lu, et al.*2025 · 11 citations*
37. **[SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification](https://md.research.elamur.ai/free/en/papers/wang-2025-sciver-evaluating-foundation-models-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chengye Wang, Yifei Shen, Zexi Kuang, et al.*2025 · 11 citations*
38. [**ERNIE 5.0 Technical Report arXiv:2602.04705**Haifeng Wang, Hua Wu, Tian Wu, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2026-ernie-5-0-technical/)
39. **[FAME: Fictional Actors for Multilingual Erasure](https://md.research.elamur.ai/free/en/papers/savelli-2026-fame-fictional-actors-multilingual-erasure/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Claudio Savelli, Moreno La Quatra, Alkis Koudounas, et al.*2026 · 7 citations*
40. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations*
41. **[ViDoRe V3: A Comprehensive Evaluation of Retrieval Augmented Generation in Complex Real-World Scenarios](https://md.research.elamur.ai/free/en/papers/loison-2026-vidore-v3-comprehensive-evaluation-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**António Loison, Quentin Macé, Antoine Edy, et al.*2026 · 7 citations*
42. **[Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization](https://md.research.elamur.ai/free/en/papers/zhang-2025-get-confused-cautiously-textual-sequence/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhaohan Zhang, Ziquan Liu, Ioannis Patras*2025 · 7 citations*
43. **[MPO: Multilingual Safety Alignment via Reward Gap Optimization](https://md.research.elamur.ai/free/en/papers/zhao-2025-mpo-multilingual-safety-alignment-reward/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weixiang Zhao, Yulin Hu, Yang Deng, et al.*2025 · 7 citations*
44. **[SVELA at EVALITA 2026: Overview of the Selective Verification of Erasure from LLM Answers Task](https://md.research.elamur.ai/free/en/papers/savelli-2026-svela-evalita-2026-overview-selective/) [Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop](https://md.research.elamur.ai/free/en/venues/evalita/)**Claudio Savelli, Moreno La Quatra, Alkis Koudounas, et al.*2026 · 6 citations*
45. **[ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering](https://md.research.elamur.ai/free/en/papers/kaur-2026-chartagent-multimodal-agent-visually-grounded/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rachneet Kaur, Nishan Srishankar, Zhen Zeng, et al.*2026 · 5 citations*
46. **[ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch](https://md.research.elamur.ai/free/en/papers/liu-2026-chartverse-scaling-chart-reasoning-reliable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Liu, Honglin Lin, Xiaoyang Wang, et al.*2026 · 5 citations*
47. **[Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2026-enhancing-auto-regressive-chain-thought/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Qifan Yu, Zhenyu He, Sijie Li, et al.*2026 · 5 citations*
48. **[Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm](https://md.research.elamur.ai/free/en/papers/li-2025-add-one-incremental-sample-selection/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhuo Li, Yuhao Du, Xiaoqi Jiao, et al.*2025 · 5 citations*
49. **[GATEAU: Selecting Influential Samples for Long Context Alignment](https://md.research.elamur.ai/free/en/papers/si-2025-gateau-selecting-influential-samples-long/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shuzheng Si, Haozhe Zhao, Gang Chen, et al.*2025 · 5 citations*
50. **[RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates](https://md.research.elamur.ai/free/en/papers/kowsher-2025-rocoft-efficient-finetuning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Md Kowsher, Tara Esmaeilbeig, Chun-Nam Yu, et al.*2025 · 5 citations*
51. **[Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/yu-2025-self-error-instruct-generalizing-errors/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Erxin Yu, Jing Li, Ming Liao, et al.*2025 · 5 citations*
52. [**Baichuan Alignment Technical Report arXiv:2410.14940**Mingan Lin, Fan Yang, Yanjun Shen, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-baichuan-alignment-technical-report/)
53. **[BLUR: A Bi-Level Optimization Approach for LLM Unlearning](https://md.research.elamur.ai/free/en/papers/reisizadeh-2026-blur-bi-level-optimization-approach/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Hadi Reisizadeh, Jinghan Jia, Zhiqi Bu, et al.*2026 · 4 citations*
54. **[OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2026-offside-benchmarking-unlearning-misinformation-multimodal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hao Zheng, Zirui Pang, Ling Li, et al.*2026 · 4 citations*
55. **[Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2026-select2reason-efficient-instruction-tuning-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cehao Yang, Xueyuan Lin, Xiaojun Wu, et al.*2026 · 4 citations*
56. **[FinChart-Bench: Benchmarking Financial Chart Comprehension in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/shu-2026-finchart-bench-benchmarking-financial-chart/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dong Shu, Haoyang Yuan, Yuchen Wang, et al.*2026 · 3 citations*
57. **[From Charts to Code: A Hierarchical Benchmark for Multimodal Models](https://md.research.elamur.ai/free/en/papers/tang-2026-charts-code-hierarchical-benchmark-multimodal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiahao Tang, Henry Hengyuan Zhao, Lijian Wu, et al.*2026 · 3 citations*
58. **[Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-harnessing-negative-signals-reinforcement-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuyao Xu, Cheng Peng, Jiangxuan Long, et al.*2026 · 3 citations*
59. **[SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe](https://md.research.elamur.ai/free/en/papers/xiao-2026-sftmix-elevating-language-model-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Xiao, Shujian Zhang, Marzyeh Ghassemi, et al.*2026 · 3 citations*
60. **[SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis](https://md.research.elamur.ai/free/en/papers/wu-2026-synthrl-scaling-visual-reasoning-verifiable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zijian Wu, Jinjie Ni, Xiangyan Liu, et al.*2026 · 3 citations*
61. **[Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search](https://md.research.elamur.ai/free/en/papers/shi-2026-efficient-multi-agent-system-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wentao Shi, Zichun Yu, Fuli Feng, et al.*2026 · 2 citations*
62. **[Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning](https://md.research.elamur.ai/free/en/papers/zhai-2026-maximizing-local-entropy-where-it/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Naixin Zhai, Pengyang Shao, Binbin Zheng, et al.*2026 · 2 citations*
63. [**MiniMax Sparse Attention arXiv:2606.13392**Xunhao Lai, Weiqi Xu, Yufeng Yang, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/lai-2026-minimax-sparse-attention/)
64. **[Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem](https://md.research.elamur.ai/free/en/papers/xiao-2026-modeling-llm-unlearning-as-asymmetric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zeguan Xiao, Siqing Li, Yong Wang, et al.*2026 · 2 citations*
65. **[POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering](https://md.research.elamur.ai/free/en/papers/xu-2026-polychartqa-benchmarking-large-vision-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yichen Xu, Liangyu Chen, Liang Zhang, et al.*2026 · 2 citations*
66. [**STEP3-VL-10B Technical Report arXiv:2601.09668**Ailin Huang, Chengyuan Yao, Chunrui Han, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/huang-2026-step3-vl-10b-technical/)
67. **[ARISE: An Adaptive Resolution-Aware Metric for Test-Time Scaling Evaluation in Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/yin-2026-arise-adaptive-resolution-aware-metric/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhangyue Yin, Qiushi Sun, Zhiyuan Zeng, et al.*2026 · 1 citation*
68. **[Chart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable Rewards](https://md.research.elamur.ai/free/en/papers/zhang-2026-chart-rl-generalized-chart-comprehension/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Xin Zhang, Xingyu Li, Rongguang Wang, et al.*2026 · 1 citation*
69. **[Erasing Without Remembering: Implicit Knowledge Forgetting in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-erasing-without-remembering-implicit-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huazheng Wang, Yongcheng Jing, Haifeng Sun, et al.*2026 · 1 citation*
70. **[Prior Beliefs Prejudice LLM-as-Judge: Evidence from Persuasion Evaluation](https://md.research.elamur.ai/free/en/papers/zahraei-2026-prior-beliefs-prejudice-llm-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pardis Sadat Zahraei, Xiaoning Wang, Nimet Beyza Bozdag, et al.*2026 · 1 citation*
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72. **[Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images](https://md.research.elamur.ai/free/en/papers/zhang-2026-structured-abstractive-reasoning-multi-modal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichi Zhang, Zhuo Chen, Lingbing Guo, et al.*2026 · 1 citation*
73. **[Why Does Low-Rank Adaptation Work for Hindi-English Code-Mixing? A Geometric Analysis](https://md.research.elamur.ai/free/en/papers/vishwakarma-2026-why-does-low-rank-adaptation/) [workshop on Challenges in Processing South Asian Languages](https://md.research.elamur.ai/free/en/venues/chipsal/)**Shashank Vishwakarma, Rakesh Kumar*2026 · 1 citation*
74. **[A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2026-survey-multimodal-mathematical-reasoning-perception/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Yang, Sihong Wu, Yilun Zhao, et al.*2026*
75. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
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77. **[Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models](https://md.research.elamur.ai/free/en/papers/shukla-2026-calibration-vs-decision-making-revisiting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Divyaksh Shukla, Ashutosh Modi*2026*
78. **[ChartDiff: A Large-Scale Benchmark for Comprehending Pairs of Charts](https://md.research.elamur.ai/free/en/papers/ye-2026-chartdiff-large-scale-benchmark-comprehending/) [Workshop on Advances in Language and Vision Research (ALVR)](https://md.research.elamur.ai/free/en/venues/alvr/)**Rongtian Ye*2026*
79. **[Compact Example-Based Explanations for Language Models](https://md.research.elamur.ai/free/en/papers/schoenegger-2026-compact-example-based-explanations-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Loris Schoenegger, Benjamin Roth*2026*
80. **[CypherSmith: Transforming Text-to-Cypher Generation for LLMs with Synthetic Data](https://md.research.elamur.ai/free/en/papers/zhang-2026-cyphersmith-transforming-text-cypher-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zeyu Zhang, Kexuan Sun, Zheng Tang, et al.*2026*
81. **[Dashboard2Code: Evaluating Multimodal Models on Reconstructing Interactive Dashboards](https://md.research.elamur.ai/free/en/papers/niu-2026-dashboard2code-evaluating-multimodal-models-reconstructing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianhao Niu, Ziyu Han, Qiguang Chen, et al.*2026*
82. **[Data Efficient RLVR via Off-Policy Influence Guidance](https://md.research.elamur.ai/free/en/papers/zhu-2026-data-efficient-rlvr-off-policy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Erle Zhu, Dazhi Jiang, Yuan Wang, et al.*2026*
83. **[Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation](https://md.research.elamur.ai/free/en/papers/yang-2026-data-efficient-adaptation-contextual-shifts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyeongjun Yang, Donghyun Kim, Seokju Hwang, et al.*2026*
84. **[Datasets for Scientific Literature Understanding: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2026-datasets-scientific-literature-understanding-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanzhe Zhang, Xun Zhao, Maodi Hu, et al.*2026*
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86. **[DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models](https://md.research.elamur.ai/free/en/papers/wu-2026-dose-data-selection-multi-modal/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Biao Wu, Yiwu Zhong, Meng Fang, et al.*2026*
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91. **[GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection](https://md.research.elamur.ai/free/en/papers/yao-2026-gast-gradient-aligned-sparse-tuning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kai Yao, Zhenghan Song, Kaixin Wu, et al.*2026*
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93. **[High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://md.research.elamur.ai/free/en/papers/wang-2026-high-throughput-memory-efficient-zeroth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Liangyu Wang, Huanyi Xie, Di Wang*2026*
94. **[InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/su-2026-instructdiff-domain-adaptive-data-selection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyou Su, He Zhu, Xiao Luo, et al.*2026*
95. **[Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/park-2026-memory-efficient-structured-backpropagation-device/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**JuneYoung Park, Yuri Hong, Seongwan Kim, et al.*2026*
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97. **[PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries](https://md.research.elamur.ai/free/en/papers/zhao-2026-plotgen-bench-evaluating-vlms-generating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Zhao, Zhen Yang, Shuaiqi Duan, et al.*2026*
98. **[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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100. **[Rewriting History: A Recipe for Interventional Analyses to Study Data Effects on Model Behavior](https://md.research.elamur.ai/free/en/papers/nadkarni-2026-rewriting-history-recipe-interventional-analyses/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Rahul Nadkarni, Yanai Elazar, Hila Gonen, et al.*2026*

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