---
title: "Who cites Wojciech Stokowiec — page 4 — 4,352 citing papers"
description: "4,352 papers of the Elamur Research library cite the works of Wojciech Stokowiec, found in their bibliographies · page 4 of 11 · 301–400; the…"
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---
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# Who cites Wojciech Stokowiec

4,766 citations of their works from 4,352 papers of our library (of the 149,439 whose bibliographies we read) · 1,041 with a page here, the most cited first · page 4 of 11 · 301–400 · [← Wojciech Stokowiec](https://md.research.elamur.ai/free/en/authors/wojciech-stokowiec/)

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301. [**Kimi-VL Technical Report arXiv:2504.07491**Kimi Team, Angang Du, Bohong Yin, et al.*2025 · 70 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-vl-technical-report/)
302. **[ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities](https://md.research.elamur.ai/free/en/papers/lu-2025-toolsandbox-stateful-conversational-interactive-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiarui Lu, Thomas Holleis, Yizhe Zhang, et al.*2025 · 70 citations*
303. **[Group-in-Group Policy Optimization for LLM Agent Training](https://md.research.elamur.ai/free/en/papers/feng-2025-group-group-policy-optimization-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lang Feng, Zhenghai Xue, Tingcong Liu, et al.*2025 · 69 citations*
304. **[Aligner: Efficient Alignment by Learning to Correct](https://md.research.elamur.ai/free/en/papers/ji-2024-aligner-efficient-alignment-learning-correct/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Boyuan Chen, Hantao Lou, et al.*2024 · 69 citations*
305. **[Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems](https://md.research.elamur.ai/free/en/papers/laban-2024-summary-haystack-challenge-long-context/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Philippe Laban, Alexander Fabbri, Caiming Xiong, et al.*2024 · 69 citations*
306. **[Apertus: Democratizing Open and Compliant LLMs for Global Language Environments](https://md.research.elamur.ai/free/en/papers/cano-2026-apertus-democratizing-open-compliant-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, et al.*2026 · 68 citations*
307. [**VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks arXiv:2504.05118**Yu Yue, Yufeng Yuan, Qiying Yu, et al.*2025 · 68 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-vapo-efficient-reliable-reinforcement-learning/)
308. **[xLAM: A Family of Large Action Models to Empower AI Agent Systems](https://md.research.elamur.ai/free/en/papers/zhang-2025-xlam-family-large-action-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jianguo Zhang, Tian Lan, Ming Zhu, et al.*2025 · 68 citations*
309. [**Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents arXiv:2408.07199**Pranav Putta, Edmund Mills, Naman Garg, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/putta-2024-agent-q-advanced-reasoning-learning/)
310. **[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://md.research.elamur.ai/free/en/papers/wang-2024-arithmetic-control-llms-diverse-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Wang, Yong Lin, Wei Xiong, et al.*2024 · 68 citations*
311. [**MagicPIG: LSH Sampling for Efficient LLM Generation arXiv:2410.16179**Zhuoming Chen, Ranajoy Sadhukhan, Zihao Ye, et al.*2024 · 68 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-magicpig-lsh-sampling-efficient-llm/)
312. **[Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration](https://md.research.elamur.ai/free/en/papers/feng-2024-modular-pluralism-pluralistic-alignment-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shangbin Feng, Taylor Sorensen, Yuhan Liu, et al.*2024 · 68 citations*
313. **[TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://md.research.elamur.ai/free/en/papers/xu-2024-theagentcompany-benchmarking-llm-agents/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Frank F. Xu, Yufan Song, Boxuan Li, et al.*2024 · 68 citations*
314. **[VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation](https://md.research.elamur.ai/free/en/papers/ku-2024-viescore-explainable-metrics-conditional-image/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Max Ku, Dongfu Jiang, Cong Wei, et al.*2024 · 68 citations*
315. [**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/)
316. **[Are Large Language Models Capable of Generating Human-Level Narratives?](https://md.research.elamur.ai/free/en/papers/tian-2024-are-large-language-models-capable/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yufei Tian, Tenghao Huang, Miri Liu, et al.*2024 · 67 citations*
317. [**MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark arXiv:2410.19168**S Sakshi, Utkarsh Tyagi, Sonal Kumar, et al.*2024 · 67 citations*](https://md.research.elamur.ai/free/en/papers/sakshi-2024-mmau-massive-multi-task-audio/)
318. **[On the Effects of Data Scale on UI Control Agents](https://md.research.elamur.ai/free/en/papers/li-2024-effects-data-scale-ui-control/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wei Li, William Bishop, Alice Li, et al.*2024 · 67 citations*
319. [**Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications arXiv:2402.05162**Boyi Wei, Kaixuan Huang, Yangsibo Huang, et al.*2024 · 66 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-assessing-brittleness-safety-alignment-pruning/)
320. [**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/)
321. **[RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval](https://md.research.elamur.ai/free/en/papers/liu-2024-retrievalattention-accelerating-long-context-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Di Liu, Meng Chen, Baotong Lu, et al.*2024 · 66 citations*
322. [**MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding arXiv:2501.18362**Yuxin Zuo, Shang Qu, Yifei Li, et al.*2025 · 65 citations*](https://md.research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/)
323. **[ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidence](https://md.research.elamur.ai/free/en/papers/wu-2024-clasheval-quantifying-tug-war-between/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kevin Wu, Eric Wu, James Zou*2024 · 65 citations*
324. [**CycleResearcher: Improving Automated Research via Automated Review arXiv:2411.00816**Yixuan Weng, Minjun Zhu, Guangsheng Bao, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/weng-2024-cycleresearcher-improving-automated-research/)
325. [**We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? arXiv:2407.01284**Runqi Qiao, Qiuna Tan, Guanting Dong, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2024-math-large-multimodal-model-achieve/)
326. [**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/)
327. **[CoT-Valve: Length-Compressible Chain-of-Thought Tuning](https://md.research.elamur.ai/free/en/papers/ma-2025-cot-valve-length-compressible-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyin Ma, Guangnian Wan, Runpeng Yu, et al.*2025 · 64 citations*
328. **[MARIO: MAth Reasoning with code Interpreter Output - A Reproducible Pipeline](https://md.research.elamur.ai/free/en/papers/liao-2024-mario-math-reasoning-code-interpreter/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Minpeng Liao, Chengxi Li, Wei Luo, et al.*2024 · 64 citations*
329. [**Llama Scope: Extracting Millions of Features from Llama-3.1-8B with Sparse Autoencoders arXiv:2410.20526**Zhengfu He, Wentao Shu, Xuyang Ge, et al.*2024 · 63 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-llama-scope-extracting-millions-features/)
330. [**Tree Search for Language Model Agents arXiv:2407.01476**Jing Yu Koh, Stephen McAleer, Daniel Fried, et al.*2024 · 61 citations*](https://md.research.elamur.ai/free/en/papers/koh-2024-tree-search-language-model-agents/)
331. [**ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models arXiv:2305.18323**Binfeng Xu, Zhiyuan Peng, Bowen Lei, et al.*2023 · 61 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-rewoo-decoupling-reasoning-observations-efficient/)
332. [**MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark arXiv:2402.04788**Dongping Chen, Ruoxi Chen, Shilin Zhang, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-mllm-judge-assessing-multimodal-llm/)
333. [**Safety Fine-Tuning at (Almost) No Cost: A Baseline for Vision Large Language Models arXiv:2402.02207**Yongshuo Zong, Ondrej Bohdal, Tingyang Yu, et al.*2024 · 60 citations*](https://md.research.elamur.ai/free/en/papers/zong-2024-safety-fine-tuning-almost-no/)
334. [**mmBERT: A Modern Multilingual Encoder with Annealed Language Learning arXiv:2509.06888**Marc Marone, Orion Weller, William Fleshman, et al.*2025 · 59 citations*](https://md.research.elamur.ai/free/en/papers/marone-2025-mmbert-modern-multilingual-encoder-annealed/)
335. [**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/)
336. [**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/)
337. **[BIG-Bench Extra Hard](https://md.research.elamur.ai/free/en/papers/kazemi-2025-big-bench-extra-hard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.*2025 · 58 citations*
338. **[OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI](https://md.research.elamur.ai/free/en/papers/huang-2024-olympicarena-benchmarking-multi-discipline-cognitive/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhen Huang, Zengzhi Wang, Shijie Xia, et al.*2024 · 56 citations*
339. **[The Art of Saying No: Contextual Noncompliance in Language Models](https://md.research.elamur.ai/free/en/papers/brahman-2024-art-saying-no-contextual-noncompliance/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.*2024 · 56 citations*
340. [**Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs arXiv:2402.14903**Aaditya K. Singh, DJ Strouse*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-tokenization-counts-impact-tokenization-arithmetic/)
341. **[InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-4khd-pioneering/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 54 citations*
342. **[M³CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/chen-2024-m-3-cot-novel-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qiguang Chen, Libo Qin, Jin Zhang, et al.*2024 · 54 citations*
343. **[SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://md.research.elamur.ai/free/en/papers/lovenia-2024-seacrowd-multilingual-multimodal-data-hub/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Holy Lovenia, Rahmad Mahendra, Salsabil Maulana Akbar, et al.*2024 · 54 citations*
344. [**Improving Language Models via Plug-and-Play Retrieval Feedback arXiv:2305.14002**Wenhao Yu, Zhihan Zhang, Zhenwen Liang, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-improving-language-models-plug-play/)
345. **[Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://md.research.elamur.ai/free/en/papers/maurya-2025-unifying-ai-tutor-evaluation-evaluation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kaushal Kumar Maurya, Kv Aditya Srivatsa, Kseniia Petukhova, et al.*2025 · 53 citations*
346. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
347. [**Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? arXiv:2406.13121**Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-long-context-language-models-subsume/)
348. [**Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning arXiv:2406.14283**Chaojie Wang, Yanchen Deng, Zhiyi Lyu, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-q-improving-multi-step-reasoning/)
349. **[Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding](https://md.research.elamur.ai/free/en/papers/belay-2025-evaluating-capabilities-large-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Tadesse Destaw Belay, Israel Abebe Azime, Abinew Ali Ayele, et al.*2025 · 52 citations*
350. [**CogVLM2: Visual Language Models for Image and Video Understanding arXiv:2408.16500**Wenyi Hong, Weihan Wang, Ming Ding, et al.*2024 · 52 citations*](https://md.research.elamur.ai/free/en/papers/hong-2024-cogvlm2-visual-language-models/)
351. **[JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation](https://md.research.elamur.ai/free/en/papers/ma-2024-janusflow-harmonizing-autoregression-rectified/) [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/)**Yiyang Ma, Xingchao Liu, Xiaokang Chen, et al.*2024 · 52 citations*
352. [**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/)
353. [**MiniMax-01: Scaling Foundation Models with Lightning Attention arXiv:2501.08313**MiniMax, Aonian Li, Bangwei Gong, et al.*2025 · 50 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-01-scaling-foundation/)
354. **[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hamish Ivison, Yizhong Wang, Jiacheng Liu, et al.*2024 · 50 citations*
355. [**RM-R1: Reward Modeling as Reasoning arXiv:2505.02387**Xiusi Chen, Gaotang Li, Ziqi Wang, et al.*2025 · 49 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-rm-r1-reward-modeling-reasoning/)
356. **[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*
357. [**Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy arXiv:2507.01352**Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao, et al.*2025 · 48 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-skywork-reward-v2-scaling/)
358. **[Attribute First, then Generate: Locally-attributable Grounded Text Generation](https://md.research.elamur.ai/free/en/papers/slobodkin-2024-attribute-first-then-generate-locally/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aviv Slobodkin, Eran Hirsch, Arie Cattan, et al.*2024 · 48 citations*
359. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies arXiv:2407.13623**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/)
360. **[Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts](https://md.research.elamur.ai/free/en/papers/briakou-2024-translating-step-step-decomposing-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Eleftheria Briakou, Jiaming Luo, Colin Cherry, et al.*2024 · 46 citations*
361. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
362. [**EuroLLM-9B: Technical Report arXiv:2506.04079**Pedro Henrique Martins, Joao Alves, Patrick Fernandes, et al.*2025 · 45 citations*](https://md.research.elamur.ai/free/en/papers/martins-2025-eurollm-9b-technical-report/)
363. [**Gemini Embedding: Generalizable Embeddings from Gemini arXiv:2503.07891**Jinhyuk Lee, Feiyang Chen, Sahil Dua, et al.*2025 · 45 citations*](https://md.research.elamur.ai/free/en/papers/lee-2025-gemini-embedding-generalizable-embeddings-gemini/)
364. [**olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models arXiv:2502.18443**Jake Poznanski, Aman Rangapur, Jon Borchardt, et al.*2025 · 44 citations*](https://md.research.elamur.ai/free/en/papers/poznanski-2025-olmocr-unlocking-trillions-tokens-pdfs/)
365. **[Stop Looking for “Important Tokens” in Multimodal Language Models: Duplication Matters More](https://md.research.elamur.ai/free/en/papers/wen-2025-stop-looking-important-tokens-multimodal/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zichen Wen, Yifeng Gao, Shaobo Wang, et al.*2025 · 44 citations*
366. **[Augmenting Math Word Problems via Iterative Question Composing](https://md.research.elamur.ai/free/en/papers/liu-2024-augmenting-math-word-problems-iterative/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Haoxiong Liu, Yifan Zhang, Yifan Luo, et al.*2024 · 44 citations*
367. [**HyperCLOVA X Technical Report arXiv:2404.01954**Kang Min Yoo, Jaegeun Han, Sookyo In, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2024-hyperclova-x-technical-report/)
368. [**LLM Pruning and Distillation in Practice: The Minitron Approach arXiv:2408.11796**Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/sreenivas-2024-llm-pruning-distillation-practice-minitron/)
369. [**NVLM: Open Frontier-Class Multimodal LLMs arXiv:2409.11402**Wenliang Dai, Nayeon Lee, Boxin Wang, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/dai-2024-nvlm-open-frontier-class-multimodal/)
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371. [**Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models arXiv:2308.11764**Mohamed Elaraby, Mengyin Lu, Jacob Dunn, et al.*2023 · 43 citations*](https://md.research.elamur.ai/free/en/papers/elaraby-2023-halo-estimation-reduction-hallucinations-open/)
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373. **[LUQ: Long-text Uncertainty Quantification for LLMs](https://md.research.elamur.ai/free/en/papers/zhang-2024-luq-long-text-uncertainty-quantification/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Caiqi Zhang, Fangyu Liu, Marco Basaldella, et al.*2024 · 42 citations*
374. [**Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models arXiv:2404.12387**Reka Team: Aitor Ormazabal, Che Zheng, Cyprien de Masson d'Autume, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/ormazabal-2024-reka-core-flash-edge-series/)
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376. [**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/)
377. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
378. [**Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling arXiv:2410.11325**Wenda Xu, Rujun Han, Zifeng Wang, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-speculative-knowledge-distillation-bridging-teacher/)
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380. **[Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://md.research.elamur.ai/free/en/papers/yona-2024-can-large-language-models-faithfully/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gal Yona, Roee Aharoni, Mor Geva*2024 · 40 citations*
381. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
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383. [**InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output arXiv:2407.03320**Pan Zhang, Xiaoyi Dong, Yuhang Zang, et al.*2024 · 39 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-internlm-xcomposer-2-5/)
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385. **[Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://md.research.elamur.ai/free/en/papers/suzgun-2026-dynamic-cheatsheet-test-time-learning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, et al.*2026 · 37 citations*
386. **[Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering](https://md.research.elamur.ai/free/en/papers/si-2025-design2code-benchmarking-multimodal-code-generation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chenglei Si, Yanzhe Zhang, Ryan Li, et al.*2025 · 37 citations*
387. **[Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers?](https://md.research.elamur.ai/free/en/papers/jin-2025-exploring-concept-depth-how-large/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyu Jin, Qinkai Yu, Jingyuan Huang, et al.*2025 · 36 citations*
388. [**Reuse, Don't Retrain: A Recipe for Continued Pretraining of Language Models arXiv:2407.07263**Jupinder Parmar, Sanjev Satheesh, Mostofa Patwary, et al.*2024 · 36 citations*](https://md.research.elamur.ai/free/en/papers/parmar-2024-reuse-don-t-retrain-recipe/)
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391. [**Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries arXiv:2409.12640**Kiran Vodrahalli; Santiago Ontanon; Nilesh Tripuraneni; Kelvin Xu; Sanil Jain; Rakesh Shivanna; Jeffrey Hui; Nishanth Dikkala; Mehran Kazemi; Bahare Fatemi; Rohan Anil; Ethan Dyer; Siamak Shakeri; Roopali Vij; Harsh Mehta; Vinay Ramasesh; Quoc Le; Ed Chi; Yifeng Lu; Orhan Firat; Angeliki Lazaridou; Jean-Baptiste Lespiau; Nithya Attaluri;, Kate Olszewska*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/vodrahalli-2024-michelangelo-long-context-evaluations-haystacks/)
392. [**On the Diversity of Synthetic Data and its Impact on Training Large Language Models arXiv:2410.15226**Hao Chen, Abdul Waheed, Xiang Li, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-diversity-synthetic-data-impact-training/)
393. [**Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models arXiv:2411.07140**Yancheng He, Shilong Li, Jiaheng Liu, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-chinese-simpleqa-chinese-factuality/)
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395. [**The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input arXiv:2501.03200**Alon Jacovi, Andrew Wang, Chris Alberti, et al.*2025 · 32 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2025-facts-grounding-leaderboard-benchmarking-llms/)
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397. **[SelfCodeAlign: Self-Alignment for Code Generation](https://md.research.elamur.ai/free/en/papers/wei-2024-selfcodealign-self-alignment-code-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiang Wei, Federico Cassano, Jiawei Liu, et al.*2024 · 32 citations*
398. [**Augmented Large Language Models with Parametric Knowledge Guiding arXiv:2305.04757**Ziyang Luo, Can Xu, Pu Zhao, et al.*2023 · 32 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-augmented-large-language-models-parametric/)
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400. [**A Survey of Small Language Models arXiv:2410.20011**Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2024-survey-small-language-models/)

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