---
title: "Who cites Daniel Tarlow — 461 papers of our library"
description: "461 papers of the Elamur Research library cite the works of Daniel Tarlow, found in their bibliographies; the most cited first."
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---
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# Who cites Daniel Tarlow

466 citations of their works from 461 papers of our library (of the 149,439 whose bibliographies we read) · 79 with a page here, the most cited first · [← Daniel Tarlow](https://md.research.elamur.ai/free/en/authors/daniel-tarlow/)

1. **[A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/) [IEEE Transactions on Neural Networks and Learning Systems](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-neural-networks-and-learning-systems/)**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,312 citations*
2. [**Graph Attention Networks arXiv:1710.10903**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 9,035 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
3. [**Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907**Thomas N. Kipf, Max Welling*2016 · 9,023 citations*](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
4. [**Inductive Representation Learning on Large Graphs arXiv:1706.02216**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,795 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
5. [**Neural Message Passing for Quantum Chemistry arXiv:1704.01212**Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, et al.*2017 · 3,793 citations*](https://md.research.elamur.ai/free/en/papers/gilmer-2017-neural-message-passing-quantum-chemistry/)
6. [**Evaluating Large Language Models Trained on Code arXiv:2107.03374**Mark Chen, Jerry Tworek, Heewoo Jun, et al.*2021 · 2,659 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
7. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
8. **[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,861 citations*
9. **[Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tao Yu, Rui Zhang, Kai Yang, et al.*2018 · 1,602 citations*
10. [**Fast Graph Representation Learning with PyTorch Geometric arXiv:1903.02428**Matthias Fey, Jan Eric Lenssen*2019 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
11. **[Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Diego Marcheggiani, Ivan Titov*2017 · 1,106 citations*
12. **[A Syntactic Neural Model for General-Purpose Code Generation](https://md.research.elamur.ai/free/en/papers/yin-2017-syntactic-neural-model-general-purpose/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengcheng Yin, Graham Neubig*2017 · 788 citations*
13. [**How Powerful are Graph Neural Networks? arXiv:1810.00826**Keyulu Xu, Weihua Hu, Jure Leskovec, et al.*2018 · 655 citations*](https://md.research.elamur.ai/free/en/papers/xu-2018-powerful-graph-neural-networks/)
14. [**A simple neural network module for relational reasoning arXiv:1706.01427**Adam Santoro, David Raposo, David G. T. Barrett, et al.*2017 · 632 citations*](https://md.research.elamur.ai/free/en/papers/santoro-2017-simple-neural-network-module-relational/)
15. **[Cross-Sentence N-ary Relation Extraction with Graph LSTMs](https://md.research.elamur.ai/free/en/papers/peng-2017-cross-sentence-n-ary-relation/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nanyun Peng, Hoifung Poon, Chris Quirk, et al.*2017 · 557 citations*
16. **[Graph-to-Sequence Learning using Gated Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/beck-2018-graph-sequence-learning-gated-graph/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Beck, Gholamreza Haffari, Trevor Cohn*2018 · 459 citations*
17. [**Diffusion-LM Improves Controllable Text Generation arXiv:2205.14217**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 440 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
18. [**Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow arXiv:1805.08949**Pengcheng Yin, Bowen Deng, Edgar Chen, et al.*2018 · 308 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-learning-mine-aligned-code-natural/)
19. **[TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation](https://md.research.elamur.ai/free/en/papers/yu-2018-typesql-knowledge-based-type-aware/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tao Yu, Zifan Li, Zilin Zhang, et al.*2018 · 303 citations*
20. **[Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://md.research.elamur.ai/free/en/papers/bogin-2019-representing-schema-structure-graph-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ben Bogin, Jonathan Berant, Matt Gardner*2019 · 272 citations*
21. [**Learning a SAT Solver from Single-Bit Supervision arXiv:1802.03685**Daniel Selsam, Matthew Lamm, Benedikt Bünz, et al.*2018 · 232 citations*](https://md.research.elamur.ai/free/en/papers/selsam-2018-learning-sat-solver-single-bit/)
22. **[Tracking the World State with Recurrent Entity Networks](https://md.research.elamur.ai/free/en/papers/henaff-2016-tracking-world-state-recurrent-entity/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Mikael Henaff, Jason Weston, Arthur Szlam, et al.*2016 · 228 citations*
23. [**A Convolutional Attention Network for Extreme Summarization of Source Code arXiv:1602.03001**Miltiadis Allamanis, Hao Peng, Charles Sutton*2016 · 198 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2016-convolutional-attention-network-extreme-summarization/)
24. **[Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/guo-2019-densely-connected-graph-convolutional-networks/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zhijiang Guo, Yan Zhang, Zhiyang Teng, et al.*2019 · 193 citations*
25. [**DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models arXiv:2210.08933**Shansan Gong, Mukai Li, Jiangtao Feng, et al.*2022 · 189 citations*](https://md.research.elamur.ai/free/en/papers/gong-2022-diffuseq-sequence-sequence-text-generation/)
26. **[Large Language Diffusion Models](https://md.research.elamur.ai/free/en/papers/nie-2025-large-language-diffusion-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shen Nie, Fengqi Zhu, Zebin You, et al.*2025 · 178 citations*
27. **[KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation](https://md.research.elamur.ai/free/en/papers/chen-2020-kgpt-knowledge-grounded-pre-training/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenhu Chen, Yu Su, Xifeng Yan, et al.*2020 · 174 citations*
28. **[A Survey of Machine Learning for Big Code and Naturalness](https://md.research.elamur.ai/free/en/papers/allamanis-2017-survey-machine-learning-big-code/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, et al.*2017 · 167 citations*
29. [**Semantic Neural Machine Translation Using AMR**Linfeng Song, Daniel Gildea, Yue Zhang, et al.*2019 · 159 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-semantic-neural-machine-translation-amr/)
30. **[PyMT5: multi-mode translation of natural language and Python code with transformers](https://md.research.elamur.ai/free/en/papers/clement-2020-pymt5-multi-mode-translation-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Colin Clement, Dawn Drain, Jonathan Timcheck, et al.*2020 · 158 citations*
31. **[Simple and Effective Masked Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/sahoo-2024-simple-effective-masked-diffusion-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, et al.*2024 · 152 citations*
32. **[DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models](https://md.research.elamur.ai/free/en/papers/he-2023-diffusionbert-improving-generative-masked-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhengfu He, Tianxiang Sun, Qiong Tang, et al.*2023 · 136 citations*
33. [**A parallel corpus of Python functions and documentation strings for automated code documentation and code generation arXiv:1707.02275**Antonio Valerio Miceli Barone, Rico Sennrich*2017 · 135 citations*](https://md.research.elamur.ai/free/en/papers/barone-2017-parallel-corpus-python-functions-documentation/)
34. **[Semantic Graphs for Generating Deep Questions](https://md.research.elamur.ai/free/en/papers/pan-2020-semantic-graphs-generating-deep-questions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liangming Pan, Yuxi Xie, Yansong Feng, et al.*2020 · 134 citations*
35. [**Learning to Represent Programs with Graphs arXiv:1711.00740**Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi*2017 · 127 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/)
36. **[AMR-To-Text Generation with Graph Transformer](https://md.research.elamur.ai/free/en/papers/wang-2020-amr-text-generation-graph-transformer/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Tianming Wang, Xiaojun Wan, Hanqi Jin*2020 · 108 citations*
37. [**Premise Selection for Theorem Proving by Deep Graph Embedding arXiv:1709.09994**Mingzhe Wang, Yihe Tang, Jian Wang, et al.*2017 · 106 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-premise-selection-theorem-proving-deep/)
38. [**Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution arXiv:2310.16834**Aaron Lou, Chenlin Meng, Stefano Ermon*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/lou-2023-discrete-diffusion-modeling-estimating-ratios/)
39. **[Simplified and Generalized Masked Diffusion for Discrete Data](https://md.research.elamur.ai/free/en/papers/shi-2024-simplified-generalized-masked-diffusion-discrete/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaxin Shi, Kehang Han, Zhe Wang, et al.*2024 · 90 citations*
40. [**Dream 7B: Diffusion Large Language Models arXiv:2508.15487**Jiacheng Ye; Zhihui Xie; Lin Zheng; Jiahui Gao; Zirui Wu; Xin Jiang; Zhenguo Li;, Lingpeng Kong*2025 · 89 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-dream-7b-diffusion-large-language/)
41. **[A General Path-Based Representation for Predicting Program Properties](https://md.research.elamur.ai/free/en/papers/alon-2018-general-path-based-representation-predicting/) [ACM SIGPLAN Notices](https://md.research.elamur.ai/free/en/venues/acm-sigplan-notices/)**Uri Alon, Meital Zilberstein, Omer Levy, et al.*2018 · 89 citations*
42. **[JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Context-based Code Generation](https://md.research.elamur.ai/free/en/papers/agashe-2019-juice-large-scale-distantly-supervised/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer*2019 · 75 citations*
43. [**code2seq: Generating Sequences from Structured Representations of Code arXiv:1808.01400**Uri Alon, Shaked Brody, Omer Levy, et al.*2018 · 72 citations*](https://md.research.elamur.ai/free/en/papers/alon-2018-code2seq-generating-sequences-structured-representations/)
44. **[Turbo Parsers: Dependency Parsing by Approximate Variational Inference](https://md.research.elamur.ai/free/en/papers/martins-2010-turbo-parsers-dependency-parsing-approximate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**André Martins, Noah Smith, Eric Xing, et al.*2010 · 69 citations*
45. [**Structural Language Models of Code arXiv:1910.00577**Uri Alon, Roy Sadaka, Omer Levy, et al.*2019 · 55 citations*](https://md.research.elamur.ai/free/en/papers/alon-2019-structural-language-models-code/)
46. [**Self-Supervised Bug Detection and Repair arXiv:2105.12787**Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt*2021 · 50 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2021-self-supervised-bug-detection-repair/)
47. **[LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models](https://md.research.elamur.ai/free/en/papers/zhu-2026-llada-1-5-variance-reduced/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fengqi Zhu, Rongzhen Wang, Shen Nie, et al.*2026 · 49 citations*
48. **[DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models](https://md.research.elamur.ai/free/en/papers/gong-2023-diffuseq-v2-bridging-discrete-continuous/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shansan Gong, Mukai Li, Jiangtao Feng, et al.*2023 · 33 citations*
49. **[Graph Representations for Higher-Order Logic and Theorem Proving](https://md.research.elamur.ai/free/en/papers/paliwal-2019-graph-representations-higher-order-logic/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Aditya Paliwal, Sarah Loos, Markus Rabe, et al.*2019 · 33 citations*
50. [**Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving arXiv:2305.16366**Xueliang Zhao, Wenda Li, Lingpeng Kong*2023 · 29 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-decomposing-enigma-subgoal-based-demonstration/)
51. [**Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed Inference arXiv:2508.02193**Yuxuan Song, Zheng Zhang, Cheng Luo, et al.*2025 · 28 citations*](https://md.research.elamur.ai/free/en/papers/song-2025-seed-diffusion-large-scale/)
52. **[Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference !](https://md.research.elamur.ai/free/en/papers/tang-2023-can-diffusion-model-achieve-better/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zecheng Tang, Pinzheng Wang, Keyan Zhou, et al.*2023 · 18 citations*
53. [**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/)
54. [**Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning arXiv:1811.00796**Mitsuru Kusumoto, Keisuke Yahata, Masahiro Sakai*2018 · 15 citations*](https://md.research.elamur.ai/free/en/papers/kusumoto-2018-automated-theorem-proving-intuitionistic-propositional/)
55. **[Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective](https://md.research.elamur.ai/free/en/papers/zhang-2025-diffusion-vs-autoregressive-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Siyue Zhang, Yilun Zhao, Liyuan Geng, et al.*2025 · 10 citations*
56. [**Beyond Autoregression: Fast LLMs via Self-Distillation Through Time arXiv:2410.21035**Justin Deschenaux, Caglar Gulcehre*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/deschenaux-2024-autoregression-fast-llms-self-distillation/)
57. **[TESS 2: A Large-Scale Generalist Diffusion Language Model](https://md.research.elamur.ai/free/en/papers/tae-2025-tess-2-large-scale-generalist/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jaesung Tae, Hamish Ivison, Sachin Kumar, et al.*2025 · 9 citations*
58. **[Attention Sinks in Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/rulli-2026-attention-sinks-diffusion-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Maximo Eduardo Rulli, Simone Petruzzi, Edoardo Michielon, et al.*2026 · 5 citations*
59. [**d2: Improving Reasoning in Diffusion Language Models via Trajectory Likelihood Estimation arXiv:2509.21474**Guanghan Wang, Gilad Turok, Yair Schiff, et al.*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-d2-improving-reasoning-diffusion-language/)
60. **[dLLM: Simple Diffusion Language Modeling](https://md.research.elamur.ai/free/en/papers/zhou-2026-dllm-simple-diffusion-language-modeling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhanhui Zhou, Lingjie Chen, Hanghang Tong, et al.*2026 · 3 citations*
61. **[TreeDiff: AST-Guided Code Generation with Diffusion LLMs](https://md.research.elamur.ai/free/en/papers/zeng-2026-treediff-ast-guided-code-generation/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Yiming Zeng, Jinghan Cao, Zexin Li, et al.*2026 · 3 citations*
62. **[Beyond Hard Masks: Progressive Token Evolution for Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/zhong-2026-beyond-hard-masks-progressive-token/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhao Zhong, Linyu Wu, Bozhen Fang, et al.*2026 · 2 citations*
63. **[Focus-dLLM: Accelerating Long-Context Diffusion LLM Inference via Confidence-Guided Context Focusing](https://md.research.elamur.ai/free/en/papers/long-2026-focus-dllm-accelerating-long-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingkun Long, Yushi Huang, Shihao Bai, et al.*2026 · 2 citations*
64. **[Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration](https://md.research.elamur.ai/free/en/papers/shen-2026-improving-throughput-diffusion-based-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jucheng Shen, Gaurav Sarkar, Yeonju Ro, et al.*2026 · 2 citations*
65. **[On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration](https://md.research.elamur.ai/free/en/papers/zhu-2026-policy-self-distillation-efficient-diffusion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huaisheng Zhu, MingYu Liu, Junze Liu, et al.*2026 · 2 citations*
66. **[Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration](https://md.research.elamur.ai/free/en/papers/zhong-2026-efficient-self-evaluation-diffusion-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhao Zhong, Linyu Wu, Wen Wang, et al.*2026 · 1 citation*
67. **[From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models](https://md.research.elamur.ai/free/en/papers/tong-2026-static-inference-dynamic-interaction-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junlong Tong, Zilong Wang, YuJie Ren, et al.*2026 · 1 citation*
68. **[Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music](https://md.research.elamur.ai/free/en/papers/su-2026-amadeus-autoregressive-model-bidirectional-attribute/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongju Su, Ke Li, Lan Yang, et al.*2026*
69. **[Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models](https://md.research.elamur.ai/free/en/papers/zou-2026-breaking-block-boundaries-anchor-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shun Zou, Yong Wang, Zehui Chen, et al.*2026*
70. **[DecoCal: Decoding with Calibration in Diffusion Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-decocal-decoding-calibration-diffusion-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fan Xu, Huixuan Zhang, Xiaojun Wan*2026*
71. **[Diff4TST: Masked Diffusion Language Model for Text Style Transfer](https://md.research.elamur.ai/free/en/papers/ma-2026-diff4tst-masked-diffusion-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinchen Ma, Gaole He, Yunshi Lan, et al.*2026*
72. **[DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2026-diffa-2-practical-diffusion-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaming Zhou, Xuxin Cheng, Shiwan Zhao, et al.*2026*
73. **[Diffusion with Truncated Blocks: Fast and High-Quality Text Generation using Truncated Block Generation](https://md.research.elamur.ai/free/en/papers/zhou-2026-diffusion-truncated-blocks-fast-high/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuyan Zhou, Weiyu Chen, James Kwok*2026*
74. **[DOS: Dependency-Oriented Sampler for Masked Diffusion Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-dos-dependency-oriented-sampler-masked/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xueyu Zhou, Yangrong Hu, Jian Huang*2026*
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