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

Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?

Razumovskaia, E., Vulić, I., & Korhonen, A. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.33 · Cited by 19

Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts

Chen, Z., Wang, X., Jiang, Y., Xie, P., Huang, F., & Tu, K. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.458 · Cited by 10

Mapping the evolution of generative AI: insights from bibliometric research

Pathak, S., & Pallasena, R. K. (2025) · Journal of Decision System · doi:10.1080/12460125.2024.2438612 · Cited by 11

Learning to Rank Context for Named Entity Recognition Using a Synthetic Dataset

Amalvy, A., Labatut, V., & Dufour, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.642 · Cited by 12

Exploring the Power of Cross-Contextual Large Language Model in Mimic Emotion Prediction

Yi, G., Yang, Y., Pan, Y., Cao, Y., Yao, J., Lv, X., Fan, C., Lv, Z., Tao, J., Liang, S., et al. (2023) · doi:10.1145/3606039.3613109 · Cited by 8

ShellGPT: Generative Pre-trained Transformer Model for Shell Language Understanding

Shi, J., Jiang, S., Xu, B., Liang, J., Xiao, Y., & Wang, W. (2023) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre59848.2023.00082 · Cited by 10

An Empirical Study of Instruction-tuning Large Language Models in Chinese

Si, Q., Wang, T., Zheng, L., Zhang, X., Cao, Y., & Wang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.269 · Cited by 13

Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs

Conia, S., Li, M., Lee, D. C., Minhas, U. I., Ilyas, I. F., & Li, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.100 · Cited by 9

MindMap: Constructing Evidence Chains for Multi-Step Reasoning in Large Language Models

Wu, Y., Han, X., Song, W., Cheng, M., & Li, F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29896 · Cited by 8

On compositional generalization of transformer-based neural machine translation

Yin, Y., Fu, L., Li, Y., & Zhang, Y. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102491 · Cited by 10

Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion

Zhu, J., Huang, H., Lin, Z., Liang, J., Tang, Z., Almubarak, K., Alharthi, M., An, B., He, J., Wu, X., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.100 · Cited by 11

ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweighting

Pan, R., Zhang, D., Zhang, H., Pan, X., Xu, M., Zhang, J., Pi, R., Wang, X., & Zhang, T. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1543 · Cited by 33

Evaluation of Medium-large Language Models at Zero-shot Closed Book Generative Question Answering

Peinl, R., & Wirth, J. (2024) · doi:10.5121/csit.2024.140106 · Cited by 8

SingaKids: A Multilingual Multimodal Dialogic Tutor for Language Learning

Liu, Z., Lin, G., Tan, H. L., Zhang, H., Lu, Y., Gao, X., Yin, S. X., Sun, H., Goh, H. H., Wong, L. H., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-industry.86 · Cited by 7

How is language intelligence evolving? A multi-dimensional survey of large language models

Chen, X., Zhou, L., Chen, J., Wang, G., & Li, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130637 · Cited by 10

Generative Models For Indic Languages: Evaluating Content Generation Capabilities

Bhat, S., Varma, V., IIIT Hyderabad Gachibowli, H. 5., & Pedanekar, N. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_021 · Cited by 7

CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks

Ismayilzada, M., Paul, D., Montariol, S., Geva, M., & Bosselut, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.607 · Cited by 14

PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching

Qi, Z., Tan, X., Shi, S., Qu, C. Y., Xu, Y., & Qi, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.45 · Cited by 9

InstructAlign: High-and-Low Resource Language Alignment via Continual Crosslingual Instruction Tuning

Cahyawijaya, S., Lovenia, H., Yu, T., Chung, W., & Fung, P. (2023) · SEALP · doi:10.18653/v1/2023.sealp-1.5 · Cited by 12

CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models

Shi, D., You, C., Huang, J., Li, T., & Xiong, D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29861 · Cited by 7

Anonymity at Risk? Assessing Re-Identification Capabilities of Large Language Models in Court Decisions

Nyffenegger, A., Stuermer, M., & Niklaus, J. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.157 · Cited by 11

Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Analysis, and Generative AI Authorship Verification Condensed Lab Overview

Ayele, A. A., Babakov, N., Bevendorff, J., Casals, X. B., Chulvi, B., Dementieva, D., Elnagar, A., Freitag, D., Fröbe, M., Korenčić, D., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71908-0_11 · Cited by 9

The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models

Li, J., Yang, L., Tang, M., Chenchong, C., Li, Z., Wang, P., & Hai, Z. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.194 · Cited by 25

Tagengo: A Multilingual Chat Dataset

Devine, P. (2024) · Workshop on Multilingual Representation Learning (MRL) · doi:10.18653/v1/2024.mrl-1.6 · Cited by 13

BSharedRAG: Backbone Shared Retrieval-Augmented Generation for the E-commerce Domain

Guan, K., Cao, Q., Sun, Y., Wang, X., & Song, R. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.62 · Cited by 11

MURI: High-Quality Instruction Tuning Datasets for Low-Resource Languages via Reverse Instructions

Köksal, A., Thaler, M., Imani, A., Üstün, A., Korhonen, A., & Schütze, H. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.18 · Cited by 16

Large Language Models and Multimodal Retrieval for Visual Word Sense Disambiguation

Kritharoula, A., Lymperaiou, M., & Stamou, G. B. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.807 · Cited by 27

HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis

Tripto, N., Uchendu, A., Le, T., Setzu, M., Giannotti, F., & Lee, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.916 · Cited by 6

Disentangling Language and Culture for Evaluating Multilingual Large Language Models

Ying, J., Tang, W., Zhao, Y., Cao, Y., Rong, Y., & Zhang, W. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1082 · Cited by 6

bgGLUE: A Bulgarian General Language Understanding Evaluation Benchmark

Hardalov, M., Atanasova, P., Mihaylov, T., Angelova, G., Simov, K., Osenova, P., Stoyanov, V., Koychev, I. K., Nakov, P., & Radev, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.487 · Cited by 7

ViWiQA: Efficient end-to-end Vietnamese Wikipedia-based Open-domain Question-Answering systems for single-hop and multi-hop questions

Nguyen, D.-H., Le, N.-K., & Nguyen, L.-M. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103514 · Cited by 6

Cell2Doc: ML Pipeline for Generating Documentation in Computational Notebooks

Mondal, T., Barnett, S., Lal, A., & Vedurada, J. (2023) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00200 · Cited by 6

Through the Thicket: A Study of Number-Oriented LLMS Derived from Random Forest Models

Romaszewski, M., Sekuła, P., Głomb, P., Cholewa, M., & Kołodziej, K. (2025) · Journal of Artificial Intelligence and Soft Computing Research · doi:10.2478/jaiscr-2025-0014 · Cited by 6

ProxyLM: Predicting Language Model Performance on Multilingual Tasks via Proxy Models

Anugraha, D., Winata, G. I., Li, C., Irawan, P. A., & Lee, E.-S. A. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.106 · Cited by 16

Wav2Prompt: End-to-End Speech Prompt Learning and Task-based Fine-tuning for Text-based LLMs

Deng, K., Sun, G., & Woodland, P. C. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.354 · Cited by 7

xPQA: Cross-Lingual Product Question Answering in 12 Languages

Shen, X., Asai, A., Byrne, B., & de Gispert, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-industry.12 · Cited by 9

A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine Translation

Attanasio, G., Plaza-del-Arco, F. M., Nozza, D., & Lauscher, A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.243 · Cited by 8

EVIT: Event-Oriented Instruction Tuning for Event Reasoning

Tao, Z., Chen, X., Jin, Z.-P., Bai, X., Haiyan, Z., & Lou, Y. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.531 · Cited by 7

Lessons from the Trenches on Reproducible Evaluation of Language Models

Biderman, S., Schoelkopf, H., Sutawika, L., Gao, L., Tow, J., Abbasi, B., Aji, A. F., Ammanamanchi, P. S., Black, S., Clive, J., et al. (2024) · arXiv · doi:10.48550/arxiv.2405.14782 · Cited by 9

Large Language Models as Kuwaiti Annotators

Alostad, H. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9020033 · Cited by 5

Towards a Retrieval Augmented Generation System for Information on Suicide Prevention*

Ascorbe, P., Campos, M., Domínguez, C., Heras, J., & Terroba-Reinares, A. R. (2023) · IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology · doi:10.1109/ieeeconf58974.2023.10404508 · Cited by 5

An Evaluation System for Large Language Models based on Open-Ended Questions

Cao, Z., Ma, Z., & Chen, M. (2024) · IEEE International Conference on Cyber Security and Cloud Computing (CSCloud) · doi:10.1109/cscloud62866.2024.00019 · Cited by 5

Data Contamination Report from the 2024 CONDA Shared Task

Sainz, O., García-Ferrero, I., Jacovi, A., Campos, J. A., Elazar, Y., Agirre, E., Goldberg, Y., Chen, W., Chim, J., Choshen, L., et al. (2024) · doi:10.18653/v1/2024.conda-1.4 · Cited by 16

Investigating Numerical Translation with Large Language Models

Tang, W., Yu, J., Li, Y., Zhao, Y., Zhang, W., Feng, W., Zhang, M., & Yang, H. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887726 · Cited by 5

Cross-Lingual Adaptation for Vision-Language Model via Multimodal Semantic Distillation

Weng, Y., He, W., Dong, J., Chaomurilige, C., Liu, X., & Liu, Z. (2025) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2025.3557678 · Cited by 5

LLM-Eraser: Optimizing Large Language Model Unlearning through Selective Pruning

Zhang, S., Zhang, L., Zhou, J., Zheng, Z., & Xiong, H. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709312 · Cited by 7

CARE: Multilingual Human Preference Learning for Cultural Awareness

Guo, G., Naous, T., Wakaki, H., Nishimura, Y., Mitsufuji, Y., Ritter, A., & Xu, W. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1669 · Cited by 5

LinguaLIFT: An Effective Two-Stage Instruction Tuning Framework for Low-Resource Language Reasoning

Zhang, H., Chen, K., Bai, X., Xiang, Y., & Zhang, M. (2026) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2026.3671636 · Cited by 9

Revisiting Instruction Fine-tuned Model Evaluation to Guide Industrial Applications

Faysse, M., Viaud, G., Hudelot, C., & Colombo, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.559 · Cited by 9

Enhancing Personalized Headline Generation via Offline Goal-conditioned Reinforcement Learning with Large Language Models

Tan, X., Cheng, L., Qiu, X., Shi, S., Cheng, Y., Chu, W., Xu, Y., & Qi, Y. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671638 · Cited by 5

An Open-source Cross-Industry and Cloud-agnostic Generative AI Platform

Ali, A. R., Kumar, K., Siddiqui, M. A., & Zahid, M. A. H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650688 · Cited by 6

Enhancing bilingual lexicon induction via harnessing polysemous words

Ding, Q., Cao, H., Feng, Z., Yang, M., & Zhao, T. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128682 · Cited by 5

Knowledge Graph Structure as Prompt: Improving Small Language Models Capabilities for Knowledge-Based Causal Discovery

Susanti, Y., & Färber, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-77844-5_5 · Cited by 8

Revision Transformers: Instructing Language Models to Change Their Values

Friedrich, F., Stammer, W., Schramowski, P., & Kersting, K. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230341 · Cited by 6

On Bilingual Lexicon Induction with Large Language Models

Li, Y., Korhonen, A., & Vulić, I. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.595 · Cited by 9

The current state of summarization

Retkowski, F. (2023) · AI critique · doi:10.1515/9783839467664-016 · Cited by 6

IndicBART Alongside Visual Element: Multimodal Summarization in Diverse Indian Languages

Kumar, R., Prakash, D., Saha, S., & Sharma, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70552-6_16 · Cited by 4

Evaluating Large Language Models along Dimensions of Language Variation: A Systematik Invesdigatiom uv Cross-lingual Generalization

Bafna, N., Murray, K., & Yarowsky, D. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.1044 · Cited by 4

Evaluating Performance and Accuracy of Large Language Models in Translating Code-Mixed Hindi to English: A Comparative Study

Nair, A. R., & Gupta, D. (2024) · IEEE India Conference · doi:10.1109/indicon63790.2024.10958340 · Cited by 4

Cracking the Code: Multi-domain LLM Evaluation on Real-World Professional Exams in Indonesia

Koto, F. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-industry.69 · Cited by 4

SecLoRA: Efficient Privacy-Preserving LLM Tuning

Bian, M. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3588837 · Cited by 4

Help Me Write a Story: Evaluating LLMs’ Ability to Generate Writing Feedback

Rashkin, H., Clark, E. W., Huot, F., & Lapata, M. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1254 · Cited by 4

Overview of PAN 2025: Voight-Kampff Generative AI Detection, Multilingual Text Detoxification, Multi-author Writing Style Analysis, and Generative Plagiarism Detection

Bevendorff, J., Dementieva, D., Fröbe, M., Gipp, B., Greiner-Petter, A., Jussi Karlgren, C., Mayerl, M., Nakov, P., Panchenko, A., Potthast, M., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04354-2_21 · Cited by 7

SinLlama - A Large Language Model for Sinhala

Aravinda, H. W. K., Sirajudeen, R., Karunathilake, S. P., de Silva, N., Kaur, R., & Ranathunga, S. (2025) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon67903.2025.11217094 · Cited by 4

Exploring generative artificial intelligence: a comprehensive guide

Shoitan, R., Moussa, M. M., Tawfik, N., Cho, Y. K., & Abdallah, M. S. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3276 · Cited by 4

Cross-Dialectal Named Entity Recognition in Arabic

Khbir, N. E., Zaratiana, U., Tomeh, N., & Charnois, T. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.12 · Cited by 4

The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages

Zhang, C., Doan, K., Liao, Q., & Abdul-Mageed, M. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.160 · Cited by 6

Semantically-Informed Regressive Encoder Score

Viskov, V., Kokush, G., Larionov, D., Eger, S., & Panchenko, A. (2023) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2023.wmt-1.69 · Cited by 4

A Tree-of-Thoughts to Broaden Multi-step Reasoning across Languages

Ranaldi, L., Pucci, G., Ranaldi, F., Ruzzetti, E. S., & Zanzotto, F. M. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.78 · Cited by 4

NCL-UoR at SemEval-2024 Task 8: Fine-tuning Large Language Models for Multigenerator, Multidomain, and Multilingual Machine-Generated Text Detection

Xiong, F., Markchom, T., Zheng, Z., Jung, S., Ojha, V., & Liang, H. (2024) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2024.semeval-1.25 · Cited by 8

USTC-BUPT at SemEval-2024 Task 8: Enhancing Machine-Generated Text Detection via Domain Adversarial Neural Networks and LLM Embeddings

Guo, Z., Jiao, K., Yao, X., Wan, Y., Li, H., Xu, B., Zhang, L., Wang, Q., Zhang, Y., & Mao, Z. (2024) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2024.semeval-1.217 · Cited by 4

Mathematical Problem Solving in Arabic: Assessing Large Language Models

Mahgoub, A., Khoriba, G. A., & ElSabry, E. A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.10.181 · Cited by 4

VFFG: Verifiable Privacy-Enhanced Federated Fine-Tuning for GPT Service

Bian, M., Ren, Y., He, G., Feng, G., & Zhang, X. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3502411 · Cited by 4

Incorporating Terminology Knowledge into Large Language Model for Domain-Specific Machine Translation

Zhao, X., Feng, C., Huang, S., Wang, J., & Xu, H. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-2292-4_6 · Cited by 4

Multilingual LLMs Inherently Reward In-Language Time-Sensitive Semantic Alignment for Low-Resource Languages

Bajpai, A., & Chakraborty, T. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i22.34515 · Cited by 5

Conic10K: A Challenging Math Problem Understanding and Reasoning Dataset

Wu, H., Hui, W., Chen, Y., Wu, W., Tu, K., & Zhou, Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.427 · Cited by 8

xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark

Zhang, C., D’Haro, L. F., Tang, C., Shi, K., Tang, G., & Li, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.371 · Cited by 6

Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients

Jin, F., Zhang, J., & Zong, C. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.22 · Cited by 4

Enhancing XML-based Compiler Construction with Large Language Models: A Novel Approach

Zahid, I. A., & Joudar, S. S. (2024) · Mesopotamian Journal of Big Data · doi:10.58496/mjbd/2024/003 · Cited by 4

FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data

Sun, H., Jin, R., Xu, S., Pan, L., Supryadi, Cui, M., Du, J., Lei, Y., Lei, Y., Shi, L., et al. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-industry.110 · Cited by 6

SIDLR: Slot and Intent Detection Models for Low-Resource Language Varieties

Kwon, S. Y., Bhatia, G., Nagoudi, E. B., Inciarte, A. A., & Abdul-Mageed, M. (2023) · Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial) · doi:10.18653/v1/2023.vardial-1.24 · Cited by 4

Proceedings of the 6th Workshop on Computational Approaches to Linguistic Code-Switching

(2023) · doi:10.18653/v1/2023.calcs-1 · Cited by 4

Few-Shot Learning of TTPs Classification Using Large Language ModelsPreprint

Fengrui, Y., & Du, Y. (2024) · Preprints.org · doi:10.20944/preprints202401.0372.v1 · Cited by 5

The Journey of Language Models in Understanding Natural Language

Liu, Y. R., Zhou, J., Sang, G., Huang, R., Zhao, X., Fang, J., Wang, T., & Li, B. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7707-5_29 · Cited by 3

Zero-Shot Fact Verification via Natural Logic and Large Language Models

Strong, M., Aly, R., & Vlachos, A. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.991 · Cited by 7

Two-stage framework for identifying and extracting Vietnamese comparative opinion quintuple extraction

Thin, D. V., Thủy, N. T. T., Hao, D. N., & Ngan, N. L.-T. (2025) · Journal of Computer Science and Cybernetics · doi:10.15625/1813-9663/20607 · Cited by 3

Prompting the Past: Exploring Zero-Shot Learning for Named Entity Recognition in Historical Texts Using Prompt-Answering LLMs

Tudor, C., Megyesi, B. B., & Östling, R. (2025) · Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL) · doi:10.18653/v1/2025.latechclfl-1.19 · Cited by 3

ALM: A Two-Stage Traffic Anomaly Detection and Analysis System via the Large Language Model

Wu, S., Dong, E., He, H., Wang, Z., Han, D., Hu, H., & Yang, J. (2025) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms57970.2025.11073622 · Cited by 3

Cross-lingual semantic information fusion for word translation enhancement

Ding, Q., Cao, H., Cao, Z., Zhou, Y., & Zhao, T. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103492 · Cited by 3

Evaluating Vision Language Model Adaptations for Radiology Report Generation in Low-Resource Languages

Salmè, M., Sicilia, R., Soda, P., & Guarrasi, V. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227552 · Cited by 3

Cross-language hotel review sentiment analysis via multi-agent federated learning with heterogeneous graph attention networks

Han, X. (2026) · Scientific Reports · doi:10.1038/s41598-026-41500-8 · Cited by 3

Multilingual Amnesia: On the Transferability of Unlearning in Multilingual LLMs

farashah, A. D., Khandelwal, A., Fauchard, M., Shi, Z., Rostamzadeh, N., & Farnadi, G. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-long.260 · Cited by 3

Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLP

Nemkova, P., Adhikari, A., Pearson, M., Sadu, V. K., & Mark, A. V. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/59n42irmw53u · Cited by 3

Learning Preference Model for LLMs via Automatic Preference Data Generation

Huang, S., Zhao, J., Li, Y., & Wang, L. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.570 · Cited by 4

Shifted PAUQ: Distribution shift in text-to-SQL

Somov, O., & Tutubalina, E. V. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.18 · Cited by 3

Dual-perspective fusion for word translation enhancement

Ding, Q., Cao, H., Cao, Z., & Zhao, T. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102815 · Cited by 3

BeliN: A novel corpus for Bengali religious news headline generation using contextual feature fusion

Osama, M., Dey, A., Ahmed, K., & Kabir, M. A. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100138 · Cited by 3

Zero-resource Speech Translation and Recognition with LLMs

Mundnich, K., Niu, X., Mathur, P., Ronanki, S., Houston, B., Elluru, V. R., Das, N., Hou, Z., Huybrechts, G., Bhatia, A., et al. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890008 · Cited by 3

Reliably Bounding False Positives: A Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction

Zhu, X., Ren, Y., Cao, Y., Lin, X., Fang, F., & Li, Y. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.601 · Cited by 3

Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling

Huang, C.-W., Li, C.-A., Hsu, T.-Y., Hsu, C.-Y., & Chen, Y.-N. (2024) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2024.findings-eacl.49 · Cited by 4

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