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

Cultural Adaptation of Recipes

Cao, Y., Kementchedjhieva, Y., Cui, R., Karamolegkou, A., Zhou, L., Dare, M., Donatelli, L. E., & Hershcovich, D. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00634 · Cited by 39

Chain-of-LoRA: Enhancing the Instruction Fine-Tuning Performance of Low-Rank Adaptation on Diverse Instruction Set

Qiu, X., Hao, T., Shi, S., Tan, X., & Xiong, Y.-J. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3377590 · Cited by 28

Chinese fine-grained financial sentiment analysis with large language models

Lan, Y., Wu, Y., Wang, X., Feng, W., & Zhang, Y. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10603-6 · Cited by 28

Beyond English: Evaluating LLMs for Arabic Grammatical Error Correction

Kwon, S., Bhatia, G., Nagoudi, E. M. B., & Abdul-Mageed, M. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.9 · Cited by 26

Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU

Koto, F., Aisyah, N., Li, H., & Baldwin, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.760 · Cited by 36

mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs

Geigle, G., Jain, A., Timofte, R., & Glavaš, G. (2024) · Workshop on Advances in Language and Vision Research (ALVR) · doi:10.18653/v1/2024.alvr-1.2 · Cited by 47

You don’t need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments

Shu, B., Zhang, L., Choi, M., Dunagan, L., Logeswaran, L., Lee, M., Card, D., & Jurgens, D. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.295 · Cited by 23

IdSarcasm: Benchmarking and Evaluating Language Models for Indonesian Sarcasm Detection

Suhartono, D., Wongso, W., & Handoyo, A. T. (2024) · IEEE Access · doi:10.1109/access.2024.3416955 · Cited by 25

SeaLLMs - Large Language Models for Southeast Asia

Nguyen, X.-P., Zhang, W., Li, X., Aljunied, M., Hu, Z., Shen, C., Chia, Y. K., Li, X., Wang, J., Tan, Q., et al. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-demos.28 · Cited by 31

COPAL-ID: Indonesian Language Reasoning with Local Culture and Nuances

Wibowo, H. A., Fuadi, E. H., Nityasya, M. N., Prasojo, R. E., & Aji, A. F. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.77 · Cited by 22

IndoCulture: Exploring Geographically Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces

Koto, F., Mahendra, R., Aisyah, N., & Baldwin, T. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00726 · Cited by 29

Transfer learning for language model adaptation

Saiful, B. M. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/169892 · Cited by 22

LLM-Pilot: Characterize and Optimize Performance of your LLM Inference Services

Lazuka, M., Anghel, A., & Parnell, T. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00022 · Cited by 31

Detecting and Mitigating Hallucinations in Multilingual Summarisation

Qiu, Y., Ziser, Y., Korhonen, A., Ponti, E. M., & Cohen, S. B. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.551 · Cited by 35

FinGPT: Large Generative Models for a Small Language

Luukkonen, R., Komulainen, V., Luoma, J., Eskelinen, A., Kanerva, J., Kupari, H.-M., Ginter, F., Laippala, V., Muennighoff, N., Piktus, A., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.164 · Cited by 53

Translate Meanings, Not Just Words: IdiomKB’s Role in Optimizing Idiomatic Translation with Language Models

Li, S., Chen, J., Yuan, S., Wu, X., Yang, H., Tao, S., & Xiao, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29817 · Cited by 32

Prompting open-source and commercial language models for grammatical error correction of English learner text

Davis, C., Caines, A., Andersen, O., Taslimipoor, S., Yannakoudakis, H., Yuan, Z., Bryant, C., Rei, M., & Buttery, P. J. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.711 · Cited by 21

BLP-2023 Task 2: Sentiment Analysis

Hasan, M. A., Alam, F., Anjum, A., Das, S., & Anjum, A. (2023) · Workshop on Bangla Language Processing (BLP) · doi:10.18653/v1/2023.banglalp-1.48 · Cited by 20

PowerPulse : Power energy chat model with LLaMA model fine‐tuned on Chinese and power sector domain knowledge

Yin, C., Du, K., Nong, Q., Zhang, H., Yang, L., Yan, B., Huang, X., Wang, X., & Zhang, X. (2023) · Expert Systems · doi:10.1111/exsy.13513 · Cited by 21

NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages

Cahyawijaya, S., Lovenia, H., Koto, F., Adhista, D., Dave, E., Oktavianti, S., Akbar, S. M., Lee, J., Shadieq, N., Cenggoro, T. W., et al. (2023) · 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/v1/2023.ijcnlp-main.60 · Cited by 28

Towards reducing hallucination in extracting information from financial reports using Large Language Models

Sarmah, B., Mehta, D. B., Pasquali, S., & Zhu, T. (2023) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3639856.3639895 · Cited by 23

Exploiting Diffusion Prior for Generalizable Dense Prediction

Lee, H.-Y., Tseng, H.-Y., Lee, H.-Y., & Yang, M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00751 · Cited by 30

M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection

Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Whitehouse, C., Afzal, O. M., Mahmoud, T., Sasaki, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.14902 · Cited by 19

BLUEX: A Benchmark Based on Brazilian Leading Universities Entrance eXams

Almeida, T. S., Laitz, T., Bonás, G. K., & Nogueira, R. F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45368-7_22 · Cited by 24

Enhancing inter-sentence attention for Semantic Textual Similarity

Zhao, Y., Xia, T., Jiang, Y., & Tian, Y. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103535 · Cited by 19

Towards Effective Disambiguation for Machine Translation with Large Language Models

Iyer, V., Chen, P., & Birch, A. (2023) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2023.wmt-1.44 · Cited by 26

Potential merits and flaws of large language models in epilepsy care: A critical review

Diessen, E. G. A. L. V., van Amerongen, R. A., Zijlmans, M., & Otte, W. M. (2024) · Epilepsia · doi:10.1111/epi.17907 · Cited by 23

LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?

Zhang, Q., Gao, C., Chen, D., Huang, Y., Huang, Y., Sun, Z., Zhang, S., Li, W., Fu, Z., Wan, Y., et al. (2024) · Findings of the Association for Computational Linguistics: NAACL 2024 · doi:10.18653/v1/2024.findings-naacl.29 · Cited by 18

Image-Based Nutritional Advisory System: Employing Multimodal Deep Learning for Food Classification and Nutritional Analysis

Cheng, S.-T., Lyu, Y.-J., & Teng, C. (2025) · Applied Sciences · doi:10.3390/app15094911 · Cited by 19

Beyond LLaVA-HD: Diving Into High-Resolution Multimodal Large Language Models

Zhang, Y., Wen, Q., Fu, C., Wang, K., Wang, X., Zhang, Z., Wang, L., & Jin, R. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3650761 · Cited by 18

Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide

Szep, M., Rueckert, D., von Eisenhart‐Rothe, R., & Hinterwimmer, F. (2026) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.627 · Cited by 18

Human-in-the-loop and large language models in smart manufacturing: Current applications, challenges, and perspectives

Bajestani, M. S., Mun, D., & Kim, D. B. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.04.027 · Cited by 21

ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

Koto, F., Li, H., Shatnawi, S., Doughman, J., Sadallah, A., Alraeesi, A., Almubarak, K., Alyafeai, Z., Sengupta, N., Shehata, S., et al. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.334 · Cited by 18

OctoPack: Instruction Tuning Code Large Language Models

Muennighoff, N., Liu, Q., Zebaze, A., Zheng, Q., Hui, B., Zhuo, T. Y., Singh, S., Tang, X., von Werra, L., & Longpre, S. (2023) · arXiv · doi:10.48550/arxiv.2308.07124 · Cited by 245

Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering

Ha, C., Asaadi, S., Karn, S. K., Farri, O., Heimann, T., & Runkler, T. A. (2024) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2024.clinicalnlp-1.21 · Cited by 17

In-Contextual Gender Bias Suppression for Large Language Models

Oba, D., Kaneko, M., & Bollegala, D. (2024) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2024.findings-eacl.121 · Cited by 17

Countering malicious content moderation evasion in online social networks: Simulation and detection of word camouflage

Huertas-García, Á., Martín, A. G., Huertas‐Tato, J., & Camacho, D. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110552 · Cited by 17

Struc-Bench: Are Large Language Models Good at Generating Complex Structured Tabular Data?

Tang, X., Zong, Y., Phang, J., Zhao, Y., Zhou, W., Cohan, A., & Gerstein, M. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-short.2 · Cited by 20

Prompt Tuning on Graph-Augmented Low-Resource Text Classification

Wen, Z., & Fang, Y. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3440068 · Cited by 20

Investigating translation for Indic languages with BLOOMZ-3b through prompting and LoRA fine-tuning

Nair, A. R., Gupta, D., & Premjith, B. (2024) · Scientific Reports · doi:10.1038/s41598-024-74617-9 · Cited by 17

The Impact of Large Language Modeling on Natural Language Processing in Legal Texts: A Comprehensive Survey

Anh, D. H., Do, D.-T., Tran, V., & Minh, N. L. (2023) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse59128.2023.10299488 · Cited by 18

Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness

Tan, X., Shi, S., Qiu, X., Qu, C., Qi, Z., Xu, Y., & Qi, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.62 · Cited by 22

Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback

Shen, W., Zheng, R., Zhan, W., Zhao, J. C., Dou, S., Gui, T., Zhang, Q., & Huang, X. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.188 · Cited by 45

A Comprehensive Analysis of Adapter Efficiency

Mundra, N., Doddapaneni, S., Dabre, R., Kunchukuttan, A., Puduppully, R., & Khapra, M. M. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3632410.3632463 · Cited by 15

Improving biomedical entity linking for complex entity mentions with LLM-based text simplification

Borchert, F., Llorca, I., & Schapranow, M.-P. (2024) · Database · doi:10.1093/database/baae067 · Cited by 18

Octopus: A Multitask Model and Toolkit for Arabic Natural Language Generation

Elmadany, A., Nagoudi, E. M. B., & Abdul-Mageed, M. (2023) · Arabic Natural Language Processing Conference (ArabicNLP) · doi:10.18653/v1/2023.arabicnlp-1.20 · Cited by 16

Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References

Tang, T., Lu, H., Jiang, Y., Huang, H., Zhang, D., Zhao, X., Kocmi, T., & Wei, F. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.367 · Cited by 15

A suite of large language models for public health infoveillance

Zhou, X., Zhou, J., Wang, C., Xie, Q., Ding, K., Mao, C., Liu, Y., Cao, Z., Chu, H., Chen, X., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02435-6 · Cited by 16

Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference

Liu, Y., Li, M., Liang, D., Li, X., Giunchiglia, F., Huang, L., Feng, X., & Guan, R. (2024) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/509 · Cited by 14

ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs

Sun, L., Tao, Z., Li, Y., & Arakawa, H. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.442 · Cited by 24

Can LLM Generate Culturally Relevant Commonsense QA Data? Case Study in Indonesian and Sundanese

Putri, R. A., Haznitrama, F. G., Adhista, D., & Oh, A. H. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.1145 · Cited by 14

Characterizing YouTube's Role in Online Gambling Promotion: A Case Study of Fortune Tiger in Brazil

Costa, J., de Oliveira, G. S., Fonseca, G., Reis, D. D. C., Teixeira, G. M., da Cunha, W. L. M., da Rocha, L. C. D., & Ferreira, C. H. G. (2025) · ACM Web Science Conference (WebSci) · doi:10.1145/3717867.3717905 · Cited by 14

Beyond English: The Impact of Prompt Translation Strategies across Languages and Tasks in Multilingual LLMs

Mondshine, I., Paz-Argaman, T., & Tsarfaty, R. (2025) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2025.findings-naacl.73 · Cited by 19

Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge

Danu, M. D., Marica, G., Karn, S. K., Georgescu, B., Mansoor, A., Ghesu, F. C., Lucian Mihai, I. T. U., Suciu, C., Grbić, S., Farri, O., et al. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.08.094 · Cited by 16

Instruction Position Matters in Sequence Generation with Large Language Models

Liu, Y., Zeng, X., Shao, C., Meng, F., & Zhou, J. N. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.693 · Cited by 23

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models

Li, C., Chen, H., Yan, M., Shen, W., Xu, H., Wu, Z., Zhang, Z., Zhou, W., Chen, Y., Chen, C., et al. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.51 · Cited by 14

LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Diao, S., Pan, R. N., Dong, H., Shum, K., Zhang, J., Xiong, W., & Zhang, T. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-demo.12 · Cited by 18

Legal-LM: Knowledge Graph Enhanced Large Language Models for Law Consulting

Shi, J., Guo, Q., Liao, Y., Wang, Y., Chen, S., & Liang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5672-8_15 · Cited by 13

MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts

Macko, D., Kopál, J., Móro, R., & Srba, I. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.36 · Cited by 28

Rethinking the Exploitation of Monolingual Data for Low-Resource Neural Machine Translation

Pang, J., Yang, B., Wong, D. F., Wan, Y., Liu, D., Chao, L. S., & Xie, J. (2023) · Computational Linguistics · doi:10.1162/coli_a_00496 · Cited by 14

Structural Priming Demonstrates Abstract Grammatical Representations in Multilingual Language Models

Michaelov, J. A., Arnett, C., Chang, T. A., & Bergen, B. K. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.227 · Cited by 18

Dólares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and English

Zhang, X., Xiang, R., Yuan, C., Feng, D., Han, W., Lopez-Lira, A., Liu, X.-Y., Qiu, M., Ananiadou, S., Peng, M., et al. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671554 · Cited by 23

BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting

Yong, Z. X., Schoelkopf, H., Muennighoff, N., Aji, A. F., Adelani, D. I., Almubarak, K., Bari, M. S., Sutawika, L., Kasai, J., Baruwa, A., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.acl-long.653 · Cited by 30

Large language models for PHM: a review of optimization techniques and applications

Yu, T., Tang, J., Yu, Q., Li, L., Liu, Y., & Poler, R. (2025) · Autonomous Intelligent Systems · doi:10.1007/s43684-025-00100-5 · Cited by 13

Leveraging Zero and Few-Shot Learning for Enhanced Model Generality in Hate Speech Detection in Spanish and English

Garcí­a-Dí­az, J. A., Pan, R., & Valencia‐García, R. (2023) · Mathematics · doi:10.3390/math11245004 · Cited by 14

SRBerta—A Transformer Language Model for Serbian Cyrillic Legal Texts

Bogdanović, M., Kocić, J., & Stoimenov, L. V. (2024) · Information · doi:10.3390/info15020074 · Cited by 15

Large Language Models As Annotators: A Preliminary Evaluation For Annotating Low-Resource Language Content

Bhat, S., & Varma, V. (2023) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2023.eval4nlp-1.8 · Cited by 12

Analysing Translation Artifacts: A Comparative Study of LLMs, NMTs, and Human Translations

Sizov, F., España-Bonet, C., van Genabith, J., Xie, R., & Chowdhury, K. D. (2024) · Conference on Machine Translation (WMT) · doi:10.18653/v1/2024.wmt-1.116 · Cited by 12

Data Augmentation and Large Language Model for Legal Case Retrieval and Entailment

Bui, M.-Q., Do, D.-T., Le, N.-K., Nguyen, D.-H., Nguyen, K.-V.-H., Anh, T. P. N., & Nguyen, L.-M. (2024) · The Review of Socionetwork Strategies · doi:10.1007/s12626-024-00158-2 · Cited by 11

Evaluating the performance of multilingual models in answer extraction and question generation

Moreno-Cediel, A., del-Hoyo-Gabaldon, J.-A., García‐López, E., García‐Cabot, A., & de-Fitero-Dominguez, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-66472-5 · Cited by 13

Do Large Language Models Understand Conversational Implicature – A Case Study with a Chinese Sitcom

Yue, S., Song, S., Cheng, X., & Hu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_24 · Cited by 15

Semantic role extraction in law texts: a comparative analysis of language models for legal information extraction

Bakker, R. M., Schoevers, A. J., van Drie, R. A. N., Schraagen, M., & de Boer, M. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09437-x · Cited by 15

Preference Tuning with Human Feedback on Language, Speech, and Vision Tasks: A Survey

Winata, G. I., Zhao, H., Das, A., Tang, W., Yao, D. D. W., Zhang, S.-X., & Sahu, S. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.17541 · Cited by 30

Comparative linguistic analysis framework of human-written vs. machine-generated text

Culda, L. C., Nerişanu, R. A., Cristescu, M. P., Mara, D. A., Bârã, A., & Oprea, S. (2025) · Connection Science · doi:10.1080/09540091.2025.2507183 · Cited by 11

Large Language Models Are Cross-Lingual Knowledge-Free Reasoners

Hu, P., Liu, S., Gao, C., Huang, X., Han, X., Feng, J., Deng, C., & Huang, S. (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.72 · Cited by 19

KMMLU: Measuring Massive Multitask Language Understanding in Korean

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