Crosslingual Generalization through Multitask Finetuning

LoRA: Low-Rank Adaptation of Large Language Models

Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., & Chen, W. (2021) · arXiv · doi:10.48550/arxiv.2106.09685 · Cited by 13,453

Binary Codes capable of currecting deletions, insertions, and reversals

Levenshtein, V. I. (1965) · Soviet physics. Doklady · Cited by 10,387

Multilingual Denoising Pre-training for Neural Machine Translation

Liu, Y., Gu, J., Goyal, N., Li, X., Edunov, S., Ghazvininejad, M., Lewis, M., & Zettlemoyer, L. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00343 · Cited by 1,762

mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer

Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.41 · Cited by 1,629

Cross-lingual Language Model Pretraining

Lample, G., & Conneau, A. (2019) · arXiv · doi:10.48550/arxiv.1901.07291 · Cited by 1,586

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2176 · Cited by 932

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

Ben Zaken, E., Goldberg, Y., & Ravfogel, S. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) · doi:10.18653/v1/2022.acl-short.1 · Cited by 765

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Liu, H., Tam, D., Muqeeth, M., Mohta, J., Huang, T., Bansal, M., & Raffel, C. (2022) · neural information processing systems · doi:10.52202/068431-0142 · Cited by 634

The Flan Collection: Designing Data and Methods for Effective Instruction TuningPreprint

Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.13688 · Cited by 553

MLQA: Evaluating Cross-lingual Extractive Question Answering

Lewis, P., Oğuz, B., Rinott, R., Riedel, S., & Schwenk, H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.653 · Cited by 509

Understanding and Mitigating Multilingual Bias in LLM-Driven Verilog Code Generation via Hard-Example In-Context Learning

Yang, G. (2026) · Electronics · doi:10.3390/electronics15112275

Reasoning in Large Language Models Across Multilingual, Long-Context, and Symbolic Settings

Nezhad, S. B. (2026) · doi:10.15760/etd.4098

Design and Deployment of an Institution-Specific Academic Chatbot Using Fine-Tuned Large Language Models

Abdelali, E. A. E. M., Salma, E.-M., Sabri, M. A., & Yahyaouy, A. (2026) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset68627.2026.11538759

When Bigger Isn’t Better: Evaluating LLMs for Arabic Sentiment Analysis

Ibrahim, M., Makki, A., Barakat, Y., Samy, N., & Alhumoud, S. O. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2kimof4u6y8x

Data-Centric Continual Pre-training for 500+ Languages: A New Bilingual Translation Corpus and Multilingual Models

Ji, S., Li, Z., Paavola, J., Luo, H., & Tiedemann, J. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.937

Candidate-Aware Retrieval and Reranking for Multiple-Choice Question Answering: Arabic as a Case Study

BOUZIANE, Y., Moukafih, Y., & Ghogho, M. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.435 · Cited by 1

Corpora Generation for Urdu Grammatical Error Correction

Ahad, S., Ezzi, B. A., Hussain, M. A., Kumar, S., & Samad, A. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.2156

Conflict-Aware Memory for Embodied Agents: Enhancing Vector Data Quality via Detection Rules

Ma, K., Wang, H., Chen, S., Cai, Y., Huangyuyu, & Jin, R. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.1306

VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation

Li, Y., Shi, Y., Ruan, Z., Zhang, D., Jiang, L., Huang, S., Chen, Y., Chen, G., & Wei, F. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.407

LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance

Fan, Y., Li, B., Li, P., Wang, Y., Mu, Y., Yang, J., Chen, X., Weng, R., Wang, J., Cai, X., et al. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.2029

Language-Aware Token Boosting: LLM Language Confusion Reduction Without Tuning

Ukarapol, T., Sarapat, P., & Chukamphaeng, N. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-short.40

Gained in Translation: Privileged Pairwise Judges Enhance Multilingual Reasoning

Sutawika, L., Swamy, G., Wu, S., & Neubig, G. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.393 · Cited by 8

Verifying the Subjective: Structured Multilingual Rewards for Low-Resource Alignment

Sha, J., & Zhu, M. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.1174

DMHM: Density-aware Manifold Learning and Hybrid Mahalanobis Energy for LLMs-generated Text Detection

Liu, T., Tian, Z., Huang, Z., Liu, T., Huang, J., Zhang, Z., Shao, C., & Li, D. (2026) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2026.acl-long.180

LLMs as Idiomatic Decompilers: Recovering High-Level Code from x86-64 Assembly for Dart

Abualazm, R., & Elhassan, A. A. (2026) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner67736.2026.00052

VIPO-MLLM: Visual-Pose Multimodal LLM for Gloss-Free Sign Language Translation

Hasanaath, A. A., Xu, B., Hossain, M. R. I., Sigal, L., & Luqman, H. (2026) · Proceedings - International Conference on Image Processing · doi:10.1109/icip61757.2026.11630033

VTaMo: Video-Text Alignment Model for Sign Language Translation

Hu, J., He, Z., Huang, H., Yang, A., & Fang, Y. D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37232-1_24

TigXMM: A Multimodal Cross-Lingual Transfer LLM Method for Low-Resource Sentiment Analysis

Gebremeskel, H. G., Feng, C., Abera, A. M., & Zhao, X. (2026) · doi:10.1145/3810987.3815534

Cross-Linguistic Epistemic Variance in Large Language Model Cognition

Nizami, M. S., Khan, M. R. S., & Bilal¹, M. (2026) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1932 · Cited by 1

Semantic Association vs. Position: Which Information Do Attention Heads Prefer?

Wang, M., Niu, S., Zhang, S., Wang, H., Song, S., Yin, Z., Yang, J., & Liang, X. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818196

Enhancing LLM generalization to high-difficulty reading comprehension through fine-tuning with intermediate-complexity data

Zhou, K., Wang, K., Wu, T., & Zhang, J. (2026) · Scientific Reports · doi:10.1038/s41598-026-66422-3

Data and Knowledge Dual-Driven Text Embedding Approaches: A Survey

Chen, X., 詹佳青, Lin, J., Liu, H., Zhang, Q., Chen, J., & Chen, H. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3721883

Instruction Fine-Tuned Language Model for Pediatric Fall Risk Assessment via Iterative Training using Generated Clinical Texts

Tabashum, T., Rifat, F. Y., Wang, S., Krzak, J. J., Kruger, K. M., Graf, A., Chafetz, R. S., Davids, J. R., Bagley, A. M., Woloff, J., et al. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00062

Toward Reliable Multilingual LLMs in JointCloud via Auto-Constriction Tuning

Dai, S., Liu, X., Deng, L., & Yu, Y. (2026) · IEEE International Conference on Joint Cloud Computing (JCC) · doi:10.1109/jcc72984.2026.00059

STAR: instruction tuning for Arabic across tasks, datasets, and models

Al-shaibani, M. S., Alyafeai, Z., & Ahmad, I. (2026) · Language Resources and Evaluation · doi:10.1007/s10579-026-09942-8

“Chi nas dal soch el sent de legn”

Signoroni, E., & Rychlý, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37249-9_9

Systematic review and comparative synthesis of cross lingual question answering systems for low resource Indic languages

Savant, S., Deshpande, U. U., & D’Silva, J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01750-x

CulturalProbe: Probing Multilingual LLM Capabilities and Limitations for Cultural Understanding in Low-Resource Languages

Binegde, G. N., Zhang, H., Li, Q., Zhang, B., Gao, C., & Gebremeskel, H. G. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.105123

What do cross-lingual clone detectors really learn? A protocol and semantic counterfactual study

(1327728), P. D., Zhao, Y., Zhang, Q., & Gong, Y. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.113141

Generalized Biomedicine Discovery

Tang, L., Yang, Y., Chen, H., Zheng, J., Chen, C., & Chen, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37324-3_24

When Does Fine-Tuning Matter for a Low-Resource Language? Evaluating Open-Weight LLMs on Kazakh Benchmarks

Mussa, A., Туймебаев, Ж. К., Мансурова, М., & Shihab, A. H. (2026) · Information · doi:10.3390/info17090921

S andhi : Fine-Grained Merging for Memory Efficient Multi-Model Serving

Gupta, V., Duran, O. K., Meda, N. S., An, I., Ananthanarayanan, G., & Iyer, A. P. (2026) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3830418.3843911

Enem-VL: A Multimodal Benchmark of Brazilian Exams for Evaluating Vision-Language Models

Soares, A. L. B., Ferreira, L. N., Leme, G. M., Scatolin, M. F., Interian, R., & Dias, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39882-6_21

Computational Linguistics and Natural Language Processing for Languages of Africa

Adelani, D. I. (2026) · Oxford University Press eBooks · doi:10.1093/9780197851463.003.1137

Hybrid Agentic Architecture for Vietnamese Sign Language: Orchestrating RAG and Syntactic Restructuring

Vy, D. N.-L., Truong, N.-N., Nguyen, V. D., Vo, D. T. D., & Le, M.-H. (2026) · International Conference on Green Technology and Sustainable Development (GTSD) · doi:10.1109/gtsd70918.2026.11722489

Proceedings of the Seventh Workshop on Insights from Negative Results in NLP

(2026) · doi:10.18653/2026.insights-1

Length and Meaning for Language Model Maps

Oyama, M., Kishino, R., Yamagiwa, H., & Shimodaira, H. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/2026.emnlp-main.1510

MutQA: A Cross-Validated Q&A Dataset for Genetic MutationsPreprint

McCourt, R., Arredondo, D., Macaulay, O., Poshiya, S., Edeh, O., Virupakshappa, K., Bach, M. N., Ruiz, E. A., Hu, Y., Tafoya, L. E., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.27.762236

Language Induced Structural Efficiency Modeling in Multilingual Prompt Code Generation

Nakkella, M., Latchipatruni, Y., Bojja, R., Dharani, A., & Dasharadhi, D. S. (2026) · International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon69735.2026.11747271

The Llama 3 Herd of Models

Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505

Consistency Regularization for Cross-Lingual Fine-Tuning

Zheng, B., Dong, L., Huang, S., Wang, W., Chi, Z., Singhal, S., Che, W., Liu, T., Song, X., & Wei, F. (2021) · doi:10.18653/v1/2021.acl-long.264 · Cited by 47

MultiFiT: Efficient Multi-lingual Language Model Fine-tuning

Eisenschlos, J. M., Ruder, S., Czapla, P., Kadras, M., Gugger, S., & Howard, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1572 · Cited by 95

Breaking the Curse of Multilinguality with Cross-lingual Expert Language Models

Blevins, T., Limisiewicz, T., Gururangan, S., Li, M., Gonen, H., Smith, N. A., & Zettlemoyer, L. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.604 · Cited by 17

Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness

Li, B., Luo, F., Haider, S., Agashe, A., Li, S., Liu, R., Miao, M. M., Ramakrishnan, S., Yuan, Y., & Callison-Burch, C. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.219 · Cited by 5

Meta-Learning for Effective Multi-task and Multilingual Modelling

Tarunesh, I., Khyalia, S., Kumar, V., Ramakrishnan, G., & Jyothi, P. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.314 · Cited by 20