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# Who cites «Curriculum Learning» · page 2

659 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/bengio-2009-curriculum-learning/)

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1. [Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum](https://arxiv.org/abs/2405.13226) *— 2024 · arXiv:2405.13226*
2. [Decoder-only Streaming Transformer for Simultaneous Translation](https://aclanthology.org/2024.acl-long.480/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [Development of Cognitive Intelligence in Pre-trained Language Models](https://aclanthology.org/2024.emnlp-main.539/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
4. [Discovering and Articulating Frames of Communication from Social Media Using Chain-of-Thought Reasoning](https://aclanthology.org/2024.eacl-long.97/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Do LLMs learn a true syntactic universal?](https://aclanthology.org/2024.emnlp-main.950/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
6. [Efficient Continual Pre-training for Building Domain Specific Large Language Models](https://aclanthology.org/2024.findings-acl.606/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
7. [Empowering Tree-structured Entailment Reasoning: Rhetorical Perception and LLM-driven Interpretability](https://aclanthology.org/2024.lrec-main.513/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
8. [Enhancing Agent Learning through World Dynamics Modeling](https://aclanthology.org/2024.findings-emnlp.202/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
9. [Enhancing Alignment using Curriculum Learning & Ranked Preferences](https://aclanthology.org/2024.findings-emnlp.754/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
10. [Exploring Curriculum Learning for Vision-Language Tasks: A Study on Small-Scale Multimodal Training](https://aclanthology.org/2024.conll-babylm.6/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
11. [FIRE: A Dataset for Financial Relation Extraction](https://aclanthology.org/2024.findings-naacl.230/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
12. [FairFlow: Mitigating Dataset Biases through Undecided Learning for Natural Language Understanding](https://aclanthology.org/2024.emnlp-main.1225/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
13. [From General LLM to Translation: How We Dramatically Improve Translation Quality Using Human Evaluation Data for LLM Finetuning](https://aclanthology.org/2024.wmt-1.17/) *— 2024 · Proceedings of the Ninth Conference on Machine Translation*
14. [Generating from AMRs into High and Low-Resource Languages using Phylogenetic Knowledge and Hierarchical QLoRA Training (HQL)](https://aclanthology.org/2024.inlg-main.7/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference*
15. [GeoGPT4V: Towards Geometric Multi-modal Large Language Models with Geometric Image Generation](https://aclanthology.org/2024.emnlp-main.44/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
16. [HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents](https://aclanthology.org/2024.findings-emnlp.433/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
17. [IT2ACL Learning Easy-to-Hard Instructions via 2-Phase Automated Curriculum Learning for Large Language Models](https://aclanthology.org/2024.lrec-main.822/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
18. [Instruction Tuning with Human Curriculum](https://aclanthology.org/2024.findings-naacl.82/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
19. [Is Child-Directed Speech Effective Training Data for Language Models?](https://aclanthology.org/2024.emnlp-main.1231/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
20. [LeanAgent: Lifelong Learning for Formal Theorem Proving](https://arxiv.org/abs/2410.06209) *— 2024 · arXiv:2410.06209*
21. [Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations](https://aclanthology.org/2024.eacl-long.56/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Metaphor Detection with Context Enhancement and Curriculum Learning](https://aclanthology.org/2024.naacl-long.149/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
23. [Multi-Step Reasoning with Large Language Models, a Survey](https://arxiv.org/abs/2407.11511) *— 2024 · arXiv:2407.11511*
24. [Neuro-Symbolic Data Generation for Math Reasoning](https://arxiv.org/abs/2412.04857) *— 2024 · arXiv:2412.04857*
25. [Orion-14B: Open-source Multilingual Large Language Models](https://arxiv.org/abs/2401.12246) *— 2024 · arXiv:2401.12246*
26. [PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction](https://aclanthology.org/2024.lrec-main.1127/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
27. [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://aclanthology.org/2024.lrec-main.1192/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
28. [SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark](https://aclanthology.org/2024.acl-short.11/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
29. [Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models](https://arxiv.org/abs/2401.01335) *— 2024 · arXiv:2401.01335*
30. [Skywork-Math: Data Scaling Laws for Mathematical Reasoning in Large Language Models -- The Story Goes On](https://arxiv.org/abs/2407.08348) *— 2024 · arXiv:2407.08348*
31. [Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization](https://arxiv.org/abs/2402.14270) *— 2024 · arXiv:2402.14270*
32. [The Role of Data Curation in Image Captioning](https://aclanthology.org/2024.eacl-long.65/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [The Unreasonable Effectiveness of Easy Training Data for Hard Tasks](https://aclanthology.org/2024.acl-long.378/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
34. [Towards Data-Efficient Language Models: A Child-Inspired Approach to Language Learning](https://aclanthology.org/2024.conll-babylm.2/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
35. [Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment](https://aclanthology.org/2024.lrec-main.1460/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
36. [TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale](https://aclanthology.org/2024.naacl-long.154/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
37. [Tuning LLMs with Contrastive Alignment Instructions for Machine Translation in Unseen, Low-resource Languages](https://aclanthology.org/2024.loresmt-1.1/) *— 2024 · Proceedings of the Seventh Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT 2024)*
38. [WavChat: A Survey of Spoken Dialogue Models](https://arxiv.org/abs/2411.13577) *— 2024 · arXiv:2411.13577*
39. [Zamba: A Compact 7B SSM Hybrid Model](https://arxiv.org/abs/2405.16712) *— 2024 · arXiv:2405.16712*
40. [A Survey of Large Language Models](https://arxiv.org/abs/2303.18223) *— 2023 · arXiv:2303.18223 · Frontiers of Computer Science, Volume 20, Issue 12, Article 2012627 (2026)*
41. [A surprisal oracle for active curriculum language modeling](https://aclanthology.org/2023.conll-babylm.22/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
42. [Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation](https://aclanthology.org/2023.findings-emnlp.597/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
43. [Baby Llama: knowledge distillation from an ensemble of teachers trained on a small dataset with no performance penalty](https://aclanthology.org/2023.conll-babylm.24/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
44. [BabyLM Challenge: Curriculum learning based on sentence complexity approximating language acquisition](https://aclanthology.org/2023.conll-babylm.25/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
45. [Breaking the Language Barrier: Improving Cross-Lingual Reasoning with Structured Self-Attention](https://aclanthology.org/2023.findings-emnlp.632/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
46. [Byte-ranked Curriculum Learning for BabyLM Strict-small Shared Task 2023](https://aclanthology.org/2023.conll-babylm.17/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
47. [CLCL: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning](https://aclanthology.org/2023.acl-long.43/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [CLIMB – Curriculum Learning for Infant-inspired Model Building](https://aclanthology.org/2023.conll-babylm.10/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
49. [Can training neural language models on a curriculum with developmentally plausible data improve alignment with human reading behavior?](https://aclanthology.org/2023.conll-babylm.9/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
50. [Code Execution with Pre-trained Language Models](https://aclanthology.org/2023.findings-acl.308/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
51. [Complexity-Guided Curriculum Learning for Text Graphs](https://aclanthology.org/2023.findings-emnlp.172/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
52. [Data Selection Curriculum for Abstractive Text Summarization](https://aclanthology.org/2023.findings-emnlp.537/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
53. [Data Selection for Language Models via Importance Resampling](https://arxiv.org/abs/2302.03169) *— 2023 · arXiv:2302.03169*
54. [Detecting Contradictory COVID-19 Drug Efficacy Claims from Biomedical Literature](https://aclanthology.org/2023.acl-short.61/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
55. [Efficient Methods for Natural Language Processing: A Survey](https://aclanthology.org/2023.tacl-1.48/) *— 2023 · Transactions of the Association for Computational Linguistics*
56. [Empower Nested Boolean Logic via Self-Supervised Curriculum Learning](https://aclanthology.org/2023.emnlp-main.847/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
57. [Eureka: Human-Level Reward Design via Coding Large Language Models](https://arxiv.org/abs/2310.12931) *— 2023 · arXiv:2310.12931*
58. [Fixed Input Parameterization for Efficient Prompting](https://aclanthology.org/2023.findings-acl.533/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
59. [Fixing MoE Over-Fitting on Low-Resource Languages in Multilingual Machine Translation](https://aclanthology.org/2023.findings-acl.897/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
60. [GPT-wee: How Small Can a Small Language Model Really Get?](https://aclanthology.org/2023.conll-babylm.2/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
61. [Generalization on the Unseen, Logic Reasoning and Degree Curriculum](https://arxiv.org/abs/2301.13105) *— 2023 · arXiv:2301.13105*
62. [How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases](https://arxiv.org/abs/2305.19905) *— 2023 · arXiv:2305.19905*
63. [How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval](https://aclanthology.org/2023.findings-emnlp.423/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
64. [HuCurl: Human-induced Curriculum Discovery](https://aclanthology.org/2023.acl-long.104/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [INFORM : Information eNtropy based multi-step reasoning FOR large language Models](https://aclanthology.org/2023.emnlp-main.216/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
66. [Improving Autoregressive Grammatical Error Correction with Non-autoregressive Models](https://aclanthology.org/2023.findings-acl.760/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
67. [In-sample Curriculum Learning by Sequence Completion for Natural Language Generation](https://aclanthology.org/2023.acl-long.666/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
68. [Knowledge Tracing: A Survey](https://arxiv.org/abs/2201.06953) *— 2023 · ACM Computing Surveys 55(11)*
69. [Leveraging Domain Adaptation and Data Augmentation to Improve Qur'anic IR in English and Arabic](https://aclanthology.org/2023.arabicnlp-1.7/) *— 2023 · Proceedings of ArabicNLP 2023*
70. [Ling-CL: Understanding NLP Models through Linguistic Curricula](https://aclanthology.org/2023.emnlp-main.834/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
71. [M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis](https://aclanthology.org/2023.emnlp-main.561/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
72. [Make Every Example Count: On the Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets](https://aclanthology.org/2023.emnlp-main.625/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
73. [Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation](https://aclanthology.org/2023.findings-emnlp.953/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
74. [Mmi01 at The BabyLM Challenge: Linguistically Motivated Curriculum Learning for Pretraining in Low-Resource Settings](https://aclanthology.org/2023.conll-babylm.23/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
75. [Modeling Easiness for Training Transformers with Curriculum Learning](https://aclanthology.org/2023.ranlp-1.101/) *— 2023 · Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing*
76. [Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering](https://aclanthology.org/2023.acl-long.449/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [MultiTabQA: Generating Tabular Answers for Multi-Table Question Answering](https://aclanthology.org/2023.acl-long.348/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
78. [NLU on Data Diets: Dynamic Data Subset Selection for NLP Classification Tasks](https://aclanthology.org/2023.sustainlp-1.9/) *— 2023 · Proceedings of the Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP)*
79. [Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning](https://aclanthology.org/2023.findings-emnlp.286/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
80. [OMNI: Open-endedness via Models of human Notions of Interestingness](https://arxiv.org/abs/2306.01711) *— 2023 · arXiv:2306.01711*
81. [On the Benefits of Learning to Route in Mixture-of-Experts Models](https://aclanthology.org/2023.emnlp-main.583/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
82. [On the effect of curriculum learning with developmental data for grammar acquisition](https://aclanthology.org/2023.conll-babylm.31/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
83. [Optimizing GPT-2 Pretraining on BabyLM Corpus with Difficulty-based Sentence Reordering](https://aclanthology.org/2023.conll-babylm.32/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
84. [PCC: Paraphrasing with Bottom-k Sampling and Cyclic Learning for Curriculum Data Augmentation](https://aclanthology.org/2023.eacl-main.5/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
85. [Paloma: A Benchmark for Evaluating Language Model Fit](https://arxiv.org/abs/2312.10523) *— 2023 · arXiv:2312.10523*
86. [Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning](https://aclanthology.org/2023.eacl-main.232/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
87. [PolyLM: An Open Source Polyglot Large Language Model](https://arxiv.org/abs/2307.06018) *— 2023 · arXiv:2307.06018*
88. [Position: AI Evaluation Should Learn from How We Test Humans](https://arxiv.org/abs/2306.10512) *— 2023 · arXiv:2306.10512*
89. [Rethinking Label Smoothing on Multi-hop Question Answering](https://aclanthology.org/2023.ccl-1.53/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
90. [Rethinking Semi-supervised Learning with Language Models](https://aclanthology.org/2023.findings-acl.347/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
91. [Search Query Spell Correction with Weak Supervision in E-commerce](https://aclanthology.org/2023.acl-industry.66/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)*
92. [Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs](https://arxiv.org/abs/2303.08114) *— 2023 · arXiv:2303.08114*
93. [Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations](https://aclanthology.org/2023.acl-long.606/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
94. [TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLMs through Translation-Assisted Chain-of-Thought Processes](https://arxiv.org/abs/2311.10797) *— 2023 · arXiv:2311.10797*
95. [The Rise and Potential of Large Language Model Based Agents: A Survey](https://arxiv.org/abs/2309.07864) *— 2023 · arXiv:2309.07864*
96. [Too Much Information: Keeping Training Simple for BabyLMs](https://aclanthology.org/2023.conll-babylm.8/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
97. [Tool Learning with Foundation Models](https://arxiv.org/abs/2304.08354) *— 2023 · arXiv:2304.08354*
98. [Understanding Translationese in Cross-Lingual Summarization](https://aclanthology.org/2023.findings-emnlp.250/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
99. [Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars](https://aclanthology.org/2023.acl-long.316/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
100. [ViStruct: Visual Structural Knowledge Extraction via Curriculum Guided Code-Vision Representation](https://aclanthology.org/2023.emnlp-main.824/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*

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