---
title: "Who cites Jean Mercat — 332 papers of our library"
description: "332 papers of the Elamur Research library cite the works of Jean Mercat, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jean-mercat/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jean-mercat/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Jean Mercat](https://md.research.elamur.ai/free/en/authors/jean-mercat/) / cited by

# Who cites Jean Mercat

352 citations of their works from 332 papers of our library (of the 149,439 whose bibliographies we read) · 72 with a page here, the most cited first · [← Jean Mercat](https://md.research.elamur.ai/free/en/authors/jean-mercat/)

1. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
2. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations*
3. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs arXiv:2501.12599**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
4. **[RedPajama: an Open Dataset for Training Large Language Models](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maurice Weber, Daniel Fu, Quentin Anthony, et al.*2024 · 260 citations*
5. **[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*
6. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model arXiv:2502.02737**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 181 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
7. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
8. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
9. [**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/)
10. [**OpenThoughts: Data Recipes for Reasoning Models arXiv:2506.04178**Etash Guha, Ryan Marten, Sedrick Keh, et al.*2025 · 122 citations*](https://md.research.elamur.ai/free/en/papers/guha-2025-openthoughts-data-recipes-reasoning-models/)
11. [**FineWeb2: One Pipeline to Scale Them All — Adapting Pre-Training Data Processing to Every Language arXiv:2506.20920**Guilherme Penedo, Hynek Kydliček, Vinko Sabolčec, et al.*2025 · 99 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2025-fineweb2-one-pipeline-scale-them/)
12. [**GLM-5: from Vibe Coding to Agentic Engineering arXiv:2602.15763**Aohan Zeng, Xin Lv, Zhenyu Hou, et al.*2026 · 95 citations*](https://md.research.elamur.ai/free/en/papers/team-2026-5-glm-5-vibe-coding/)
13. **[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*
14. [**Small Language Models: Survey, Measurements, and Insights arXiv:2409.15790**Zhenyan Lu, Xiang Li, Dongqi Cai, et al.*2024 · 88 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-small-language-models-survey-measurements/)
15. **[MoBA: Mixture of Block Attention for Long-Context LLMs](https://md.research.elamur.ai/free/en/papers/lu-2025-moba-mixture-block-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Enzhe Lu, Zhejun Jiang, Jingyuan Liu, et al.*2025 · 80 citations*
16. **[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*
17. **[Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://md.research.elamur.ai/free/en/papers/su-2025-nemotron-cc-transforming-common-crawl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Su, Kezhi Kong, Ying Lin, et al.*2025 · 65 citations*
18. [**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/)
19. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
20. [**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/)
21. [**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/)
22. [**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/)
23. [**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/)
24. **[Paloma: A Benchmark for Evaluating Language Model Fit](https://md.research.elamur.ai/free/en/papers/magnusson-2023-paloma-benchmark-evaluating-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ian Magnusson, Akshita Bhagia, Valentin Hofmann, et al.*2023 · 42 citations*
25. [**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/)
26. [**Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models arXiv:2504.03624**NVIDIA: Aaron Blakeman, Aarti Basant, Abhinav Khattar, et al.*2025 · 38 citations*](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-h-family-accurate-efficient/)
27. **[Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 31 citations*
28. [**InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models arXiv:2503.06692**Yuchen Yan, Yongliang Shen, Yang Liu, et al.*2025 · 29 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-inftythink-breaking-length-limits-long/)
29. **[Towards Effective and Efficient Continual Pre-training of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Chen, Zhipeng Chen, Jiapeng Wang, et al.*2025 · 29 citations*
30. [**Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning arXiv:2512.20848**NVIDIA: Aaron Blakeman, Aaron Grattafiori, Aarti Basant, et al.*2025 · 27 citations*](https://md.research.elamur.ai/free/en/papers/blakeman-2025-nemotron-3-nano-open-efficient/)
31. [**Distillation Scaling Laws arXiv:2502.08606**Dan Busbridge, Amitis Shidani, Floris Weers, et al.*2025 · 26 citations*](https://md.research.elamur.ai/free/en/papers/busbridge-2025-distillation-scaling-laws/)
32. [**MiniCPM-V 4.5: Cooking Efficient MLLMs via Architecture, Data, and Training Recipe arXiv:2509.18154**Tianyu Yu, Zefan Wang, Chongyi Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/yu-2025-minicpm-v-4-5/)
33. [**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/)
34. [**MiniCPM4: Ultra-Efficient LLMs on End Devices arXiv:2506.07900**MiniCPM Team, Chaojun Xiao, Yuxuan Li, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/minicpm-team-2025-minicpm4-ultra-efficient-llms/)
35. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
36. **[Resolving Discrepancies in Compute-Optimal Scaling of Language Models](https://md.research.elamur.ai/free/en/papers/porian-2024-resolving-discrepancies-compute-optimal-scaling/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Tomer Porian, Mitchell Wortsman, Jenia Jitsev, et al.*2024 · 19 citations*
37. **[Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum](https://md.research.elamur.ai/free/en/papers/pouransari-2024-dataset-decomposition-faster-llm-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hadi Pouransari, Chun-Liang Li, Jen-Hao Rick Chang, et al.*2024 · 16 citations*
38. [**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/)
39. [**Rethinking Reflection in Pre-Training arXiv:2504.04022**Essential AI: Darsh J Shah, Peter Rushton, Somanshu Singla, et al.*2025 · 15 citations*](https://md.research.elamur.ai/free/en/papers/shah-2025-rethinking-reflection-pre-training/)
40. **[Fast and Effective On-Policy Distillation from Reasoning Prefixes](https://md.research.elamur.ai/free/en/papers/zhang-2026-fast-effective-policy-distillation-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dongxu Zhang, Zhichao Yang, Sepehr Janghorbani, et al.*2026 · 12 citations*
41. [**Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining arXiv:2412.15285**Steven Feng, Shrimai Prabhumoye, Kezhi Kong, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/feng-2024-maximize-data-s-potential-enhancing/)
42. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/)
43. [**Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thought arXiv:2510.04230**Guijin Son, Donghun Yang, Hitesh Laxmichand Patel, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/son-2025-pushing-multilingual-reasoning-models-language/)
44. [**Bilingual Adaptation of Monolingual Foundation Models arXiv:2407.12869**Gurpreet Gosal, Yishi Xu, Gokul Ramakrishnan, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/gosal-2024-bilingual-adaptation-monolingual-foundation-models/)
45. [**Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic arXiv:2412.04277**Zaid Alyafeai, Michael Pieler, Hannah Teufel, et al.*2024 · 6 citations*](https://md.research.elamur.ai/free/en/papers/alyafeai-2024-arabic-stable-lm-adapting-stable/)
46. **[PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning](https://md.research.elamur.ai/free/en/papers/hu-2026-pacore-learning-scale-test-time/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingcheng Hu, Yinmin Zhang, Shijie Shang, et al.*2026 · 5 citations*
47. **[Aleph-Alpha-GermanWeb: Improving German-language LLM pre-training with model-based data curation and synthetic data generation](https://md.research.elamur.ai/free/en/papers/burns-2026-aleph-alpha-germanweb-improving-german/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Thomas F Burns, Letitia Parcalabescu, Stephan Waeldchen, et al.*2026 · 4 citations*
48. **[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.*2026 · 4 citations*
49. **[Controllable LLM Reasoning via Sparse Autoencoder-Based Steering](https://md.research.elamur.ai/free/en/papers/fang-2026-controllable-llm-reasoning-sparse-autoencoder/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yi Fang, Wenjie Wang, Mingfeng Xue, et al.*2026 · 3 citations*
50. **[MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://md.research.elamur.ai/free/en/papers/yu-2026-mathagent-adversarial-evolution-constraint-graphs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiong Yu, Jun Rao, Guhan Chen, et al.*2026 · 3 citations*
51. **[Lizard: An Efficient Linearization Framework for Large Language Models](https://md.research.elamur.ai/free/en/papers/nguyen-2026-lizard-efficient-linearization-framework-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chien Van Nguyen, Huy Huu Nguyen, Ruiyi Zhang, et al.*2026 · 2 citations*
52. **[QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/min-2026-quco-rag-quantifying-uncertainty-pre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dehai Min, Kailin Zhang, Tongtong Wu, et al.*2026 · 2 citations*
53. [**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/)
54. **[The olmOCR Project: Building Fully Open OCR using VLMs](https://md.research.elamur.ai/free/en/papers/poznanski-2026-olmocr-project-building-fully-open/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jake Poznanski, Kyle Lo, Luca Soldaini*2026 · 2 citations*
55. **[Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training](https://md.research.elamur.ai/free/en/papers/yang-2026-data-mixing-agent-learning-re/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kailai Yang, Xiao Liu, Lei Ji, et al.*2026 · 1 citation*
56. [**KletterMix: Climbing Toward High-Quality German Pretraining Data - The Full Report arXiv:2606.03773**Maurice Kraus, Ruben Harle, Sebastian Sztwiertnia, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/kraus-2026-klettermix-climbing-toward-high-quality/)
57. **[Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories](https://md.research.elamur.ai/free/en/papers/liu-2026-learning-contrasts-synthesizing-reasoning-paths/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiyang Liu, Zhirui Chen, Xi Wang, et al.*2026 · 1 citation*
58. **[On the Step Length Confounding in LLM Reasoning Data Selection](https://md.research.elamur.ai/free/en/papers/wang-2026-step-length-confounding-llm-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bing Wang, Rui Miao, Chen Shen, et al.*2026 · 1 citation*
59. **[Where Reasoning Breaks: Logic-Aware Path Selection by Controlling Logical Connectives in LLMs Reasoning Chains](https://md.research.elamur.ai/free/en/papers/park-2026-where-reasoning-breaks-logic-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seunghyun Park, Yuanyuan Lei*2026 · 1 citation*
60. **[Biomed-Enriched: Data-Efficient Biomedical Pretraining via Paragraph-Level Annotation](https://md.research.elamur.ai/free/en/papers/touchent-2026-biomed-enriched-data-efficient-biomedical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rian Touchent, Nathan Godey, Éric Villemonte de la Clergerie*2026*
61. **[Dialectal Filtering: Synthesizing Kurdish Corpora for Low-Resource Varieties by Utilizing “Noise” in Large Textual Data](https://md.research.elamur.ai/free/en/papers/schuler-2026-dialectal-filtering-synthesizing-kurdish-corpora/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Christian Schuler, Raman Ahmad, Ānrán Wáng, et al.*2026*
62. [**Probing the Dynamics of Syntactic Ability Acquisition Throughout LLM Pretraining**Hiroshi Matsuda, Masayuki Asahara*2026*](https://md.research.elamur.ai/free/en/papers/matsuda-2026-probing-dynamics-syntactic-ability-acquisition/)
63. **[Pru-CoT: Towards Efficient Reasoning Distillation via Pruning Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/liu-2026-pru-cot-efficient-reasoning-distillation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Han Liu, Shuotian Ma, Hui Li, et al.*2026*
64. **[ReasMark: A Robust Watermark for Attributing LLM Reasoning Under Knowledge Distillation Attacks](https://md.research.elamur.ai/free/en/papers/lv-2026-reasmark-robust-watermark-attributing-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peizhuo Lv, Ruihua Zhou, Yunpeng Li, et al.*2026*
65. **[Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives](https://md.research.elamur.ai/free/en/papers/liu-2026-rethinking-data-mixture-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yajiao Liu, Congliang Chen, Junchi Yang, et al.*2026*
66. **[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*
67. **[SciPedia: Unlocking the Value of Scientific Data for Pre-training](https://md.research.elamur.ai/free/en/papers/qin-2026-scipedia-unlocking-value-scientific-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yiwei Qin, Zhen Huang, Tiantian Mi, et al.*2026*
68. **[SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification](https://md.research.elamur.ai/free/en/papers/tong-2026-selecting-over-tokens-curating-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xin Tong, Weidong Zhang, Jiaang Li, et al.*2026*
69. **[Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scale](https://md.research.elamur.ai/free/en/papers/liu-2026-systematic-evaluation-quality-synthetic-clinical/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Jinghui Liu, Sarvesh Soni, Anthony Nguyen*2026*
70. **[TARo: Token-level Adaptive Routing for LLM Test-time Alignment](https://md.research.elamur.ai/free/en/papers/rai-2026-taro-token-level-adaptive-routing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Arushi Rai, Qiang Zhang, Hanqing Zeng, et al.*2026*
71. **[UniGeM: Unifying Data Selection and Mixing via Geometric Exploration and Mining](https://md.research.elamur.ai/free/en/papers/wang-2026-unigem-unifying-data-selection-mixing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Changhao Wang, Yunfeiyu, Xinhao Yao, et al.*2026*
72. [**SmolKalam: Ensemble Quality-Filtered Translation at Scale for High Quality Arabic Post-Training Data arXiv:2511.18411**Sultan Alrashed, Chadi Helwe, Francesco Orabona*2025*](https://md.research.elamur.ai/free/en/papers/alrashed-2025-smolkalam-ensemble-quality-filtered-translation/)

260 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/jean-mercat/) — Jean Mercat