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

AIpom at SemEval-2024 Task 8: Detecting AI-produced Outputs in M4

Shirnin, A., Andreev, N., Mikhailov, V., & Artemova, E. (2024) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2024.semeval-1.238 · Cited by 1

Normalizing without Modernizing: Keeping Historical Wordforms of Middle French while Reducing Spelling Variants

Rubino, R., Gerlach, J., Mutal, J., & Bouillon, P. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.215

On the Way to Gentle AI Counselor: Politeness Cause Elicitation and Intensity Tagging in Code-mixed Hinglish Conversations for Social Good

Priya, P., Singh, G. V., Firdaus, M., Agrawal, J., & Ekbal, A. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.290

ICLFP-NMT: Neural Machine Translation for ICL Flexible Prompt

Wu, N., Wu, W.-H., Ji, Y., Liu, Y., Lu, M., & Liu, N. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5669-8_12

ABCD Team at SOMD 2024: Software Mention Detection in Scholarly Publications with Large Language Models

Xuan, P. N., Minh, Q. T., & Văn, T. Đ. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65794-8_19

ALADAN at IWSLT24 Low-resource Arabic Dialectal Speech Translation Task

Kheder, W. B., Jon, J., Beyer, A., Messaoudi, A., Affan, R., Barras, C., Tychonov, M., & Gauvain, J. (2024) · International Conference on Spoken Language Translation (IWSLT) · doi:10.18653/v1/2024.iwslt-1.25

AlexUNLP-MZ at ArAIEval Shared Task: Contrastive Learning, LLM Features Extraction and Multi-Objective Optimization for Arabic Multi-Modal Meme Propaganda Detection

Zaytoon, M., El-Makky, N., & Torki, M. (2024) · Arabic Natural Language Processing Workshop (WANLP) · doi:10.18653/v1/2024.arabicnlp-1.53

Proceedings of the 1st Workshop on Data Contamination (CONDA)

(2024) · doi:10.18653/v1/2024.conda-1

Motivation and Best Practices for Machine Learning Designers and Testers

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_3

Optimal Integration of the ML Solution in the Business Decision Process

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_11

ML Testing

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_5

Unit Test vs. System Test of ML-Based Systems

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_4

Principles of Drift Detection and ML Solution Retraining

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_6

Sequential Drift Detection

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_8

Drift Detection by Measuring Distribution Differences

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_7

A Detailed Chatbot Example

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_13

Introduction

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_1

Scientific Analysis of ML Systems

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_2

A Framework Analysis for Alternating Components and Drift

Ackerman, S., Barash, G., Farchi, E., Raz, O., & Shehory, O. M. (2024) · Springer eBooks · doi:10.1007/978-3-031-70008-8_10

Fame Bias – Large Language Models Change Their Judgement Depending on Personal Name

Ji, H., & Rzepka, R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0122-6_2

Counter Turing Test (CT2): Investigating AI-Generated Text Detection for Hindi - Ranking LLMs based on Hindi AI Detectability Index (ADI_hi)

Kavathekar, I., Rani, A., Chamoli, A., Kumaraguru, P., Sheth, A., & Das, A. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.282

A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectives

Li, Z., Ji, S., Mickus, T., Segonne, V., & Tiedemann, J. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.888

ICPR 2024 Competition on Multilingual Claim-Span Identification

Poddar, S., Paul, B., Basu, M., & Ghosh, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-80139-6_10

An Improvement of Large Language Model for Vietnamese Traffic Law Question Answer System

Phu, Q. Q., Trong, T. D., Huy, H. P., Quoc, T. N., Hoang, V. T., & Zhaparov, M. (2024) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-75593-4_22

Continuous Pre-training of TAPAS Model with E-commerce Structured Data

Chen, X., Du, S., & Qiu, Y. (2024) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba62489.2024.10868941

Performance Evaluation of NLP Models for European Portuguese: Multi-GPU/Multi-node Configurations and Optimization Techniques

Santos, D., Miquelina, N., Schmidt, D., Quaresma, P., & Nogueira, V. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-1551-3_20

VisionLingua: Empowering Multimodal Understanding with Cutting-Edge Language Models

Kamboj, D., Gola, K. K., & Arya, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-83796-8_4

Enhancing word distinction for bilingual lexicon induction with generalized antonym knowledge

Ding, Q., Cao, H., Feng, Z., & Zhao, T. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113247

Enhancing Aspect-Based Sentiment Analysis for Portuguese Using Instruction Tuning

Pereira, G., Barbosa, L., Moreira, J., de Melo, T., & da Silva, A. (2024) · doi:10.5753/eniac.2024.245109

Towards Robust Knowledge Representations in Multilingual LLMs for Equivalence and Inheritance based Consistent Reasoning

Arora, G. S., Merugu, S., Jain, S., & Saxena, V. (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.394 · Cited by 1

TPC: One Investigation Reveals the Exploration of Offensive Language

Li, C., & Long, J. (2024) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv62410.2024.00024

Schema Matching Using Federated Learning on a Hybrid Feature Set

Shepelev, A. E., & Stupnikov, S. A. (2025) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661825700142

CODE-SWITCHED RELATION EXTRACTION: A NOVEL DATASET AND TRAINING METHODOLOGY

Chen, M. L., & Siddiqui, M. (2025) · International Journal of Modern Computer Science and IT Innovations · doi:10.55640/ijmcsit-v02i02-01

An Analysis of the Training Data Impact for Domain-Adapted Tokenizer Performances—The Case of Serbian Legal Domain Adaptation

Bogdanović, M., Gligorijević, M. F., Kocić, J., & Stoimenov, L. V. (2025) · Applied Sciences · doi:10.3390/app15137491

Investigating Recent Large Language Models for Vietnamese Machine Reading Comprehension

Nguyen, A. D., Phi, H. M., Ngo, A. V., Trieu, L. H., & Nguyen, T. P. (2024) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse63888.2024.11063515

Incorporating Cross-Lingual Fine-Grained Dual Alignment Features in Domain-Specific Translation Quality Estimation

Ye, N., & Wang, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-9994-0_18

MAF-CPR: LLM-Based Multi-agent Framework for Complex Pull Request Review in GitHub

Han, F., Peng, J., Wang, W., & Xia, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0020-8_21

BelarusianGLUE: Towards a Natural Language Understanding Benchmark for Belarusian

Aparovich, M., Harytskaya, V., Poritski, V., Volchek, O., & Smrž, P. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.25

ATLANTIS: Weak-to-Strong Learning via Importance Sampling

Liu, Y., Wang, G., Li, S., Song, F., & Sun, X. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.52

MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset

Shaier, S., Baker, G. A., Sridhar, C., Hunter, L., & von der Wense, K. (2025) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2025.findings-acl.209 · Cited by 1

AfroCS-xs: Creating a Compact, High-Quality, Human-Validated Code-Switched Dataset for African Languages

Olaleye, K., Oncevay, A., Sibue, M., Zondi, N., Terblanche, M., Mapikitla, S., Lastrucci, R., Smiley, C., & Marivate, V. N. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1601

Front Matter

Jia, R., Wallace, E., Huang, Y., Pimentel, T., Maini, P., Dankers, V., Wei, J., & Lesci, P. (2025) · doi:10.18653/v1/2025.l2m2-1.0

Can LLMs Ground when they (Don’t) Know: A Study on Direct and Loaded Political Questions

Lachenmaier, C., Sieker, J., & Zarrieß, S. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.728 · Cited by 1

Effectively PAIRing LLMs with Online Marketing via Progressive Prompting Augmentation

Gan, C., Yang, D., Hu, B., Liu, Z., Shen, Y., Zhang, Z., Gu, J., Zhou, J., & Zhang, G. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00323

Adaptive Prompt Modifier for Text-to-Image Model

Ju, X., Xu, Y., & Yang, G. (2025) · IEEE International Conference on Multimedia and Expo workshops · doi:10.1109/icmew68306.2025.11152055

IWPO: Sample Importance Weight-Based Human Preference Optimization for Large Language Models

Ma, T., Luo, X., & Yuan, Y. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3615458

Writing style change detection: state of the art, challenges, and research opportunities

Alsheddi, A. S., & Menaï, M. E. B. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11402-8 · Cited by 1

RLMI: A Black-Box Model Inversion Attack Method Against Large Language Models for Text Classification

Xie, Y., & Gao, Y. (2025) · Chinese Journal of Electronics · doi:10.23919/cje.2025.00.037

B-REASO: A Multi-Level Multi-Faceted Bengali Evaluation Suite for Foundation Models

Hosain, M. T., & Morol, M. K. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.492

SampleMix: A Sample-wise Pre-training Data Mixing Strategy by Coordinating Data Quality and Diversity

Xi, X., Kong, D., Yang, J., Yang, J., Chen, Z., Wang, W., Wang, J., Cai, X., Zhang, S., & Ye, W. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.741

Let’s Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models’ Understanding of Sports

Singh, P. K., Kumar, N., Ghosh, A., Pasad, K., Soni, K., Jaishwal, M., Saha, S., Alfarozi, S. A. I., Abagissa, A. T., Pasupa, K., et al. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.769 · Cited by 1

Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?

Shen, S., Singh, M., Logeswaran, L., Lee, M., Lee, H., & Mihalcea, R. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.7 · Cited by 3

Enhancing Vector Data Quality through Negative Learning for Retrieval-augmented Large Models

Yao, L., Ma, K., Jin, R., Zheng, H., & Wang, D. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229352

Imitate, Reward and Introspect: Enhancing Neural Machine Translation by Utilizing Large Language Models as Environments

Zhao, Y., Zhang, Y., Xiang, L., Zhang, J., Zhou, Y., & Zong, C. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3623904

A Novel Translation-Driven Approach to Enhance LLM Performance on Low-Resource Languages

Ofer, M., Zamler, O., & Azaria, A. (2025) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai66417.2025.00052

A COMPARISON OF TOKENIZER PERFORMANCES – THE CASE OF SERBIAN LEGAL DOMAIN ADAPTATION

Bogdanović, M., Gligorijević, M. F., Kocić, J., & Stoimenov, L. V. (2025) · Facta Universitatis Series Automatic Control and Robotics · doi:10.22190/fuacr250429008b

Contrastive Scenario-Aware Meta Prompting for Multi-scenario Recommendation

Li, A., Hu, J., Ding, K., Zhang, X., Zhou, J., & He, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-4158-4_30

Content-aware instruction for zero-shot Chinese criminal legal case retrieval: a large language model-based framework

Zhou, Z., & Li, X. (2026) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09497-z

Beyond Language-Specific Neurons: The Challenge of Identifying Speech-Specific Neurons in Multimodal LLMs

Park, N., Lee, C. H., Yeom, J., Kim, H., & Yoon, S. (2026) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2026.3657641

Exploring efficient zero-shot synthetic dataset generation for Information Retrieval

Almeida, T., & Matos, S. (2024) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2024.findings-eacl.81

Instruction Tuning

Zhao, W. X., Zhou, K., Li, J., Tang, T., & Wen, J.-R. (2025) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-981-96-6259-3_7

NarrativeMind: A Dynamic Neural-Symbolic Decoder for Culturally-Authentic Arabic Story Generation

Ibrahim, M. N., Gervás, P., & Méndez, G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-15538-2_7

Benchmarking Language Model Surprisal for Eye-Tracking Predictions in Brazilian Portuguese

Alves, D. (2025) · doi:10.26615/978-954-452-104-2-002

Domain Adaptation of Large Language Model Tokenizers—A Comparison of Performances

Bogdanović, M., Gligorijević, M. F., Kocić, J., & Stoimenov, L. V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-04890-5_21

Prompt-Driven Cross-Lingual Dialogue Generation for Low-Resource Indian Languages: A Case Study on Bengali and Hindi

Bal, S., & Mandal, L. (2025) · International Conference on Artificial Intelligence for Computing, Astronomy and Renewable Energy (AICARE) · doi:10.1109/aicare66005.2025.11402672

Proceedings of the Third Ukrainian Natural Language Processing Workshop (UNLP) @ LREC-COLING 2024

(2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5bwu58575ghh

Design and Application of an Evaluation System for Large Language Models

Shen, Y., Cao, Z., & Chen, M. (2025) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai67447.2025.11412740

Governance-Aware Hybrid Fine-Tuning for Multilingual Large Language Models

Qi, H., Huang, C., Dai, Z., & Gao, Y. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401808

ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses

Zong, C. F., Chen, Y., Lu, W., Shao, J., Zhuang, Y., Chang, H., & Zhuang, Y. (2025) · International Joint Conference on Natural Language Processing and Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (IJCNLP-AACL) · doi:10.18653/v1/2025.ijcnlp-long.51

A Multi-Aspect Evaluation of Tokenizer Adaptation Methods for Large Language Models on Russian

Andriushchenko, G. D., Godunova, M. E., Иванов, В. В., Kuzmin, D. S., Parinov, A. A., Schenikova, A. Y., & Zhemchuzhina, E. V. (2025) · Doklady Mathematics · doi:10.1134/s1064562425700395

LangGPS: Language Separability Guided Data Pre-Selection for Joint Multilingual Instruction Tuning

Ye, Y., Feng, X., Feng, X., Huang, L., Ma, W., Hong, Q., Lu, Y., Tang, D., Tu, D., & Qin, B. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i40.40743

Layer-wise Swapping for Generalizable Multilingual Safety

Shin, H., & Hwang, W. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-long.98

M-MiniGPT4: Multilingual VLLM Alignment via Translated Data

Han, S. H. E., Mohamed, Y., & Elhoseiny, M. (2026) · Workshop on African Natural Language Processing (AfricaNLP) · doi:10.18653/v1/2026.africanlp-main.2

Can Linguistically Related Languages Guide LLM Translation in Low-Resource Settings?

Ramasethu, A., Garg, R., Allu, N., Fartale, H., & Chan, D. L. (2026) · Workshop on Technologies for Machine Translation of Low-Resource Languages (LoResMT) · doi:10.18653/v1/2026.loresmt-1.14

Ranking Human and LLM Texts Using Locality Statistics

Wang, Y., Ding, C., & He, H. (2026) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2026.findings-eacl.283

A unified multimodal GenAI platform integrating GraphRAG multi-agent systems and custom language models for intelligent document processing and knowledge synthesis

Parihar, B. S., Singh, T. P., Gonnade, G., Dewangan, B. K., Shelke, N. M., & Pimpalkar, A. P. (2026) · Scientific Reports · doi:10.1038/s41598-026-47145-x

Fine-Tuning

Wang, Z., Zhu, Q., Chen, X., Sim, M. Y., Zhou, W., Hao, S., & Fu, J. (2026) · Synthesis lectures on human language technologies · doi:10.1007/978-3-032-06264-2_6

Generating and answering questions across text and knowledge graphs

Han, K. (2024) · theses.fr (ABES) · doi:10.70675/605774e1z4ab3z4bc8z9628ze56d226162f9

Understanding and Evaluating Unsupervised Cross-lingual Embeddings in the General and in the Clinical Domains

Gaschi, F. (2023) · theses.fr (ABES) · doi:10.70675/134784a8z1491z4726za4a2z2e367ad63c88

Unified Multimodal and Multilingual Retrieval via Multi-Task Learning With NLU Integration

Zhang, X., Lina, Z., Chen, L., Guangyao, L., Shuai, N., Xu, J., Runyu, S., Ying, H., & Zhang, G. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11464291

Beyond Literal Meaning: How LLMs Interpret Yemeni Proverbs

Thmer, N., Al-Laith, A. M. A., & Shoaib, M. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4hxnxxxq5iu2

Sentiment Analysis and Language Models for Kwanyama

Nakashole, N. (2026) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/4whctbu5acfp

Comma: A multi-task and multi-lingual dataset of constitutional verdicts

Ragazzi, L., Frisoni, G., Moro, G., Italiani, P., Molfetta, L., & Folin, V. (2026) · Artificial Intelligence and Law · doi:10.1007/s10506-026-09520-x

HateMediator: Fine-Tuning Large Language Models for Counter-Hate Speech via Multiturn Mediations

Wu, X.-K., Ai, L., Li, Y., Lu, L., Xie, J., Wang, Y., Luo, J., Zeng, D., Chen, M., & Fortino, G. (2026) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2026.3687425

Resilience

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0009

Topic Modeling and Word Embedding

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.005.0001

Linguistic Patterns in Online Conspiracy Theory

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0001

Opportunities for Future Research

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0010

Cultural Toolkit

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0005

From Mindsets and Algorithms to a Cultural Toolkit

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0004

Why Do Online Conspiracy Theories Matter?

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0002

Endorsement

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.002.0001

Online Conspiracy Theorizing as Conversational Coordination

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0008

Fake News, Rumors, Folk Theories

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0003

Online Conspiracy Rabbit Hole as a Semantic Cascade

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0006

Dedication

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.002.0006

How We Studied Conspiracy Theories

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.005.0002

Preface

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.002.0007

Ctrl+Alt+Doubt

Rao, H., & Greve, H. R. (2026) · doi:10.1093/9780197772324.001.0001

Linguistic Signature of Superspreaders

Rao, H., & Greve, H. R. (2026) · Oxford University Press eBooks · doi:10.1093/9780197772324.003.0007

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