Self-Instruct: Aligning Language Models with Self-Generated Instructions

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372

Distilling the Knowledge in a Neural Network

Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929

Training language models to follow instructions with human feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208

Training Language Models to Follow Instructions with Human Feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.243 · Cited by 3,096

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv · doi:10.48550/arxiv.2104.08691 · Cited by 3,092

Self-Training With Noisy Student Improves ImageNet Classification

Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01070 · Cited by 2,393

Scaling Instruction-Finetuned Language Models

Chung, H. W., Hou, L., Longpre, S., Zoph, B., Tay, Y., Fedus, W., Li, Y., Wang, X., Dehghani, M., Brahma, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2210.11416 · Cited by 1,441

Finetuned Language Models Are Zero-Shot Learners

Wei, J., Bosma, M., Zhao, V. Y., Guu, K., Yu, A. W., Lester, B., Du, N., Dai, A. M., & Le, Q. V. (2021) · arXiv · doi:10.48550/arxiv.2109.01652 · Cited by 1,101

Trustworthiness Evaluation of Large Language Models

Chen, P., & Liu, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-76770-8_12 · Cited by 2

Knowledge Distillation ≈ Label Smoothing: Fact or Fallacy? Label Smoothing: Fact or Fallacy?

Sultan, M. A. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.271 · Cited by 3

Penalty Decoding: Well Suppress the Self-Reinforcement Effect in Open-Ended Text Generation

Zhu, W.-H., Hao, H., & Wang, R. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.78 · Cited by 4

From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models

Yan, J., Wang, C., Zhang, T., He, X., Huang, J., & Zhang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.828 · Cited by 5

Hate Speech Detection using CoT and Post-hoc Explanation through Instruction-based Fine Tuning in Large Language Models

Gupta, P., Jain, N., & Bhat, A. (2024) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic60222.2024.10575336 · Cited by 2

SELF-EXPERTISE: Knowledge-based Instruction Dataset Augmentation for a Legal Expert Language Model

Kim, M., Jung, H., & Koo, M. (2024) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2024.findings-naacl.69 · Cited by 2

Aligning Large Language Models via Fine-grained Supervision

Xu, D., Qiu, L., Kim, M., Ladhak, F., & Do, J. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-short.62 · Cited by 7

A Three-Phases-LORA Finetuned Hybrid LLM Integrated with Strong Prior Module in the Education Context

Chen, Z., Liu, C., & Duan, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72344-5_16 · Cited by 3

Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and Multi-Source Supervision

Wu, S., Chen, Z., & Su, Q. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681533 · Cited by 2

FLIP-80M: 80 Million Visual-Linguistic Pairs for Facial Language-Image Pre-Training

Li, Y., Hou, X., Dezhi, Z., Shen, L., & Zhao, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681287 · Cited by 2

MedQAS: A Medical Question Answering System Based on Finetuning Large Language Models

Duong, B. T., & Lê, T. H. (2024) · Communications in computer and information science · doi:10.1007/978-981-96-0434-0_21 · Cited by 2

Aligners: Decoupling LLMs and Alignment

Ngweta, L., Agarwal, M., Maity, S., Gittens, A., Sun, Y., & Yurochkin, M. (2024) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2024.findings-emnlp.808 · Cited by 6

Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models

Singla, S., Wang, Z., Liu, T., Ashfaq, A., Hu, Z., & Xing, E. P. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.1220 · Cited by 12

PhiloGPT: A Philology-Oriented Large Language Model for Ancient Chinese Manuscripts with Dunhuang as Case Study

Zhang, Y., He, B., Chen, Y., Li, H., Yue, H., Zhang, S., Dou, H., Yan, J., Liu, Z., Zhang, Y., et al. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.163 · Cited by 2

Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

Zhang, S., Zhu, P., Ma, M., Wang, J., Sun, Y., Li, D., Wang, J., Guo, Q., Hua, X., Zhu, L., et al. (2024) · IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) · doi:10.1109/issrew63542.2024.00048 · Cited by 3

A Particle Swarm Optimization‐Based Approach Coupled With Large Language Models for Prompt Optimization

Hsieh, P., & Lee, W. (2025) · Expert Systems · doi:10.1111/exsy.70049 · Cited by 3

How Important is Domain-Specific Language Model Pretraining and Instruction Finetuning for Biomedical Relation Extraction?

Brokman, A., & Kavuluru, R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97141-9_6 · Cited by 4

The ShareLM Collection and Plugin: Contributing Human-Model Chats for the Benefit of the Community

Don-Yehiya, S., Choshen, L., & Abend, O. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-demo.17 · Cited by 2

SGIC: A Self-Guided Iterative Calibration Framework for RAG

Chen, G., Yao, Y., Chao, L. S., Liu, X., & Wong, D. F. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1376 · Cited by 2

A fine-grained evaluation framework for language models: Combining pointwise grading and pairwise comparison

Li, Y., & Sun, Y. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104270 · Cited by 3

Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement

Zhang, Z., Cao, Y., & Liao, L. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755596 · Cited by 2

TaskEval: Assessing Difficulty of Code Generation Tasks for Large Language Models

Tambon, F., Nikanjam, A., Zid, C., Khomh, F., & Antoniol, G. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3773285 · Cited by 2

Enhancing and Assessing Instruction-Following with Fine-Grained Instruction Variants

Yang, J., Liu, H., Guo, W., Yu, X., & Niu, D. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3761322 · Cited by 7

Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text

Mitra, A., Yang, Z., Druhl, E., Goodwin, R., & Yu, H. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1418 · Cited by 4

CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models

Sim, M. Y., Dai, X., Rybinski, M., & Karimi, S. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.68 · Cited by 2

Improving Human Resources’ Efficiency with a Generative AI-Based Resume Analysis Solution

Tran, T. T., Nguyen, T. G., Dang, T. H., & Yoshinaga, Y. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8296-7_25 · Cited by 2

Lateral AI: Simulating Diversity in Virtual Communities

Hadzic, F., & Krayneva, M. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8391-9_4 · Cited by 2

Innovative Design of Large Language Model in the Medical Field Based on chip-PromptCBLUE

Ling, H., Yin, B., Ge, C., Shi, P., Wang, J., Xian, F., & Quan, F. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-1717-0_2 · Cited by 2

Multi-modal Medical Q&A System

zhi, W. (2024) · International Conference on Computer and Multimedia Technology (ICCMT) · doi:10.1145/3675249.3675321 · Cited by 2

Aligning Prompts with Ranking Goals: A Technical Review of Prompt Engineering for LLM-Based RecommendationsPreprint

Raja, R., Vats, A., & Roy, S. S. (2025) · Preprints.org · doi:10.20944/preprints202509.1959.v1 · Cited by 3

TT-SI: Self-Improving LLM Agents with Test-Time Training

Acikgoz, E. C., Qian, C., Ji, H., Hakkani-Tür, D., & Tur, G. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.462 · Cited by 1

MedSentry: Understanding and Mitigating Safety Risks in Medical LLM Multi-Agent Systems

Chen, K., Zhen, T., Wang, H., Liu, K., Li, X., Huo, J., Yang, T., Xu, J., Dong, W., & Gao, Y. (2025) · arXiv · doi:10.48550/arxiv.2505.20824 · Cited by 32

From Self-Learning to Self-Evolving Architectures in Large Language Models: A Short Survey

Sapkota, R., Roumeliotis, K. I., Pokhrel, S., & Karkee, M. (2025) · doi:10.36227/techrxiv.174838070.09235849/v1 · Cited by 1

Fine-Tuning of Small/Medium LLMs for Business QA on Structured Data

Ragab, R., & Altahhan, A. (2024) · International Journal on Natural Language Computing · doi:10.5121/ijnlc.2024.13301 · Cited by 1

: A Voting-Based Paradigm for Enhancing Retrieval Augmented Generation

Guan, W., Li, X., Lu, J., & Zhou, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78119-3_9 · Cited by 1

The First Instruct-Following Large Language Models for Hungarian

Yang, Z. G., Dodé, R., Ferenczi, G., Hatvani, P., Héja, E., Madarász, G., Ligeti-Nagy, N., Sárossy, B., Szaniszló, Z., Váradi, T., et al. (2024) · IEEE Conference on Information Technology and Data Science (CITDS) · doi:10.1109/citds62610.2024.10791364 · Cited by 2

Design and Application of a Multi-task Legal Large Language Model

Han, M., Xu, J., Li, Y., & Zhu, F. (2024) · International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI) · doi:10.1109/ricai64321.2024.10911477 · Cited by 1

SelfBehave, Generating a Synthetic Behaviour-Driven Development Dataset using SELF-INSTRUCT

Galloy, M., Balfroid, M., Vanderose, B., & Devroey, X. (2025) · IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) · doi:10.1109/icstw64639.2025.10962479 · Cited by 1

Interactive Agent Foundation Model: A Meta-Learning Strategy

Dwivedi, M., & Dwivedi, A. K. (2025) · International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC) · doi:10.1109/isacc65211.2025.10969383 · Cited by 1

Fine-Tuning LLMs for Anesthesiology via Compositional Data Generation

Li, Y., Zhan, Y., Yu, B., Wang, Z., Zhang, C., Zhu, D., Zhou, C., Zhou, B., Ding, L., Liu, W., et al. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3567602 · Cited by 1

Active Instruction Tuning for Large Language Models with Reference-Free Instruction Selection

Zhang, Q., Chen, J., & Ma, C. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8180-8_2 · Cited by 1

Towards Robust Synthetic Data Generation for Simplification of Text in French

Tsourakis, N. (2025) · Machine Learning and Knowledge Extraction · doi:10.3390/make7030068 · Cited by 1

ILD-Slider: A Parameter-Efficient Model for Identifying Progressive Fibrosing Interstitial Lung Disease from Chest CT Slices

Zhang, J., Wada, S., Sugimoto, K., Niitsu, T., Fukushima, K., Kida, H., Wang, B., Konishi, S., Okada, K., Nakashima, Y., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11100353 · Cited by 1

TACOS: Open Tagging and Comparative Scoring for Instruction Fine-Tuning Data Selection

He, X., Yu, H., Sun, Q., Cheng, A., Zhang, T., Liu, C., & Guo, S. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11209076 · Cited by 1

Instruction Fine-Tuning Through the Lens of Verbatim Memorization

Zhang, J., Lin, C.-H., & Lee, S. (2026) · Electronics · doi:10.3390/electronics15020377 · Cited by 1

A dual syntax bidirectional interactive network for fine-grained sentiment analysis

Ke, T., Liu, J., Chen, Z., & Chen, K. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.113944 · Cited by 1

Towards Proactive Social Robots: Distilling Visual Knowledge from Large Vision-Language Models

Simone, G. D., Roveda, L., Saggese, A., & Vento, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00262 · Cited by 1

Two Shots are Enough: Reliable Constrained Generation with LLMs

Mondal, M., Dolamic, L., Cudré-Mauroux, P., & Audiffren, J. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402199 · Cited by 1

CLEF 2026 JOKER Track

Магдановна, Е. Л., Kuzmin, I., Vachharajani, P., Miller, T., Bosser, A.-G., & Kamps, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-21321-1_34 · Cited by 1

A Turkish Educational Crossword Puzzle Generator

Zeinalipour, K., Keptiğ, Y. G., Maggini, M., Rigutini, L., & Gori, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64312-5_27 · Cited by 2

HumanRankEval: Automatic Evaluation of LMs as Conversational Assistants

Gritta, M., Λάμπουρας, Γ., & Iacobacci, I. (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.456 · Cited by 1

RTaC: A Generalized Framework for Tooling

Bhavsar, N., Thakur, A., Singh, A. L., & Patwa, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-65794-8_5 · Cited by 1

Adapting BLOOM to a new language: A case study for the Italian

Basile, P., Siciliani, L., Musacchio, E., Polignano, M., & Semeraro, G. M. (2024) · Italian Journal of Computational Linguistics · doi:10.4000/125nl · Cited by 1

Rethinking the Evaluation of In-Context Learning for LLMs

Yu, G., Liu, L., Yu, M., Yu, Y., & Ao, X. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-main.779 · Cited by 1

SAAS: Solving Ability Amplification Strategy for Enhanced Mathematical Reasoning in Large Language Models

Kim, H., Gim, G., Kim, Y., Kim, J., Kim, B., Lee, W., & Park, C. (2024) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2024.emnlp-industry.15 · Cited by 1

Chinese Grammatical Error Correction via Large Language Model Guided Optimization Training

Liu, X., Li, Y., & Yu, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8367-0_31 · Cited by 1

ChatQCD: Let Large Language Models Explore QCD

Sulc, A., & Connor, P. L. S. (2024) · International Conference on High Energy Physics (ICHEP) · doi:10.22323/1.476.1053 · Cited by 1

Advancing Multimodal LLMs: A Focus on Geometry Problem Solving Reasoning and Sequential Scoring

Jaiswal, R., Anand, A., & Shah, R. R. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700262 · Cited by 1

Multimodal Mamba: A Versatile Multimodal Model for Seamless Integration into Diverse Downstream Tasks

Li, Z., Zhu, G., Yi, D., & Wang, J. (2024) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3704323.3704364 · Cited by 2

Developing a Multilingual Dataset and Evaluation Metrics for Code-Switching: A Focus on Hong Kong’s Polylingual Dynamics

Xie, P., & Chen, K. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890166 · Cited by 1

PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines

Vir, R., Shankar, S., Chase, H., Hinthorn, W., & Parameswaran, A. (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.213 · Cited by 1

AssistEM: Domain Instruction Tuning for Enhanced Entity Matching

Mugeni, J. B., Lynden, S. J., Amagasa, T., & Matono, A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8186-0_10 · Cited by 1

Long Story Generation Challenge

Mikhaylovskiy, N. (2023) · International Conference on Natural Language Generation · doi:10.18653/v1/2023.inlg-genchal.2 · Cited by 1

Forecasts and insights into Japan’s fiscal future: Machine learning-based projections of city-level taxpayer numbers and total income from 2020 to 2100

Li, C., Keeley, A. R., & Managi, S. (2025) · Machine Learning with Applications · doi:10.1016/j.mlwa.2025.100699 · Cited by 2

Distilling Closed-Source LLM’s Knowledge for Locally Stable and Economic Biomedical Entity Linking

Ai, Y., Ning, Z., Dai, W., Wang, P., Du, Y., Cui, W., Liu, K., & Zhou, Y. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0027-7_9 · Cited by 1

Generating Code Tours Using Locally-Runnable LLMs

Balfroid, M. (2025) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3696630.3731462 · Cited by 1

GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization

Gu, Z., Chen, X., Shi, X., Wang, T., Zheng, S., Li, T., Feng, H., & Xiao, Y. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.13 · Cited by 1

Adapting Large Language Models for Movie Domain with Narrative Understanding Tasks

Shen, S., & Garg, A. (2025) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2025.conll-1.13 · Cited by 1

Prompt Refinement or Fine-tuning? Best Practices for using LLMs in Computational Social Science Tasks

Møller, A. G., & Aiello, L. M. (2025) · Workshop on Social Influence in Conversations (SICon) · doi:10.18653/v1/2025.sicon-1.2 · Cited by 1

MLAN: Language-Based Instruction Tuning Preserves and Transfers Knowledge in Multimodal Language Models

Tu, J., Ni, Z., Crispino, N., Yu, Z., Bendersky, M., Gunel, B., Jia, R., Liu, X., Lyu, L., Song, D. X., et al. (2025) · Workshop on Towards Knowledgeable Foundation Models (KnowFM) · doi:10.18653/v1/2025.knowllm-1.6 · Cited by 1

Grammar and Gameplay-Aligned RL for Game Description Generation with LLMs

Tanaka, T., & Simo‐Serra, E. (2025) · IEEE Conference on Games (CoG) · doi:10.1109/cog64752.2025.11114202 · Cited by 1

Recent advances in finetuning multimodal large language models

Wang, Z., Li, L., & Chen, L. (2025) · AI Magazine · doi:10.1002/aaai.70025 · Cited by 3

Development of computationally feasible hospital-specific large language models using parameter-efficient and preference alignment techniques

Patil, S. S., Ramteke, M., Verma, M., Chandra, T., & Rathore, A. S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11600-z · Cited by 1

CrossLM: A Data-Free Collaborative Fine-Tuning Framework for Large and Small Language Models

Deng, Y., Qiao, Z., Zhang, Y., Ma, Z., Liu, Y., & Ren, J. (2025) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3711875.3729128 · Cited by 3

Elastic Architecture Search for Efficient Language Models

Wang, S. (2025) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme59968.2025.11210235 · Cited by 1

Self-preference: An Automated Method for Preference-Aligned Data Constructed from Business Metrics

Gao, F., Zhang, X., Ni, B., Wang, C., & Chen, L. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-2725-0_19 · Cited by 1

Training LLMs for Optimization Modeling via Iterative Data Synthesis and Structured Validation

Wu, Y., Zhang, Y., Wu, Y., Wang, Y., Zhang, J., & Cheng, J. (2025) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2025.findings-emnlp.691 · Cited by 4

Demystifying Synthetic Data in LLM Pre-training: A Systematic Study of Scaling Laws, Benefits, and Pitfalls

Kang, F., Ardalani, N., Kuchnik, M., Emad, Y., Elhoushi, M., SenGupta, S., Li, S.-W. D., Raghavendra, R., Jia, R., & Wu, C.-J. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.544 · Cited by 22

Can LLMs Really Help Query Understanding In Web Search? A Practical Perspective

Ye, D., Qin, Y., Tian, B., Fan, J., Liu, J., Liang, H., & Ma, J. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760842 · Cited by 3

OceanAgent: A small-scale multi-modal assistant for ocean exploration

Xu, Y., Liu, Y., Shang, J., Lin, J., & Ma, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129640 · Cited by 1

Benchmarking Fine-Tuning Strategies for LLaMA: A Multi-Dimensional Evaluation for National Security Contexts

Brown, D. L., Rawat, D. B., LaDuke, M., & Lanigan, B. J. (2025) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri66576.2025.00023 · Cited by 1

Beyond Perfect APIs: Evaluating LLM Agents Under Realistic API Complexity

Kim, D., Ren, Z., Hao, J., Sun, Z., Wang, L., Ma, X., Han, X., Yin, J., Ji, H., Shen, W., et al. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.48550/arxiv.2601.00268 · Cited by 1

Refining Positive and Toxic Samples for Dual Safety Self-Alignment of LLMs With Minimal Human Interventions

Xu, J., Nan, G., Guan, S., Leng, S., Liu, Y., Wang, Z., Ma, Y., Zhou, Z., Hou, Y., & Tao, X. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3652843 · Cited by 1

Large Language Models and Creative Content Design: a case study of email marketing at Wine Access

Dubé, J.-P., & Xu, A. (2026) · Quantitative Marketing and Economics · doi:10.1007/s11129-025-09303-9 · Cited by 1

PsyAgent: Constructing Human-like Agents Based on Psychological Modeling and Contextual Interaction

Meng, Z., & Chen, K. (2026) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/2026.emnlp-main.25 · Cited by 1

Building a marine reasoning large model: a method based on structured chain-of-thought fine-tuning and knowledge graph

Lin, Y., Du, Z., Sun, X., Li, X., Liu, C., Zheng, X., Liu, E., Chen, M., Liu, X., Xuan, H., et al. (2026) · Intelligent Marine Technology and Systems · doi:10.1007/s44295-025-00091-2 · Cited by 1

Knowledge intensive agents

Liu, Z., Huang, P., Xu, Z., Li, X., Liu, S., Peng, C., Xin, H., Yan, Y., Wang, S., Han, X., et al. (2026) · AI Open · doi:10.1016/j.aiopen.2026.02.002 · Cited by 1

Integrating Large Language Models with Cybersecurity Education

Yan, W., Chattopadhyay, S., Zhang, L., & Amresh, A. (2026) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3770761.3777269 · Cited by 1

Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments

Venkatesh, V., Kamanuru, V. R., Kumar, L., & Kothari, N. (2025) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon67450.2025.11381090 · Cited by 1

DP-Prox: A Robust and Differentially Private Framework for Federated Instruction Tuning of Small LLMs on 8 GB Edge Devices

Venkatesh, V., & Liu, Y. (2026) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc67433.2026.11393831 · Cited by 1

Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles

Zugarini, A., Zeinalipour, K., Kadali, S. S., Maggini, M., Gori, M., & Rigutini, L. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/362v2thhybvm · Cited by 1

Learning to ask and answer in specialized documents: Exemplifying through modular integrated construction regulatory documents

Wei, Y., Li, X., Huang, Z., Lin, Z., & Zhang, N. (2026) · Computers in Industry · doi:10.1016/j.compind.2026.104462 · Cited by 1

Dynamic Temperature Scheduler for Knowledge Distillation

Islam, S. U., Ahad, J. I., Rahman, F., Amin, M. R., Mohammed, N., & Rahman, S. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11402644 · Cited by 1

AdapSNE: Adaptive Fireworks-Optimized and Entropy-Guided Dataset Sampling for Edge DNN Training

Zhao, B., Liu, H., Yuan, Z., Zhu, L., Yang, F., Xie, L., Xia, T., Zhao, W., & Ren, P. (2026) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2026.3667183 · Cited by 1

LiteLong: Resource-Efficient Long-Context Data Synthesis for LLMs

Jia, J., Wu, X., Gao, C., Chen, Z., Lin, Z., Li, Z., Wang, W., Xu, H., Jin, D., Zhang, D., et al. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i37.40390 · Cited by 2

PrAda-GAN: A Private Adaptive Generative Adversarial Network with Bayes Network Structure

Jia, K., Ma, Y., Li, Y., & Wang, F. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i27.39382 · Cited by 1

Flora: Effortless Context Construction to Arbitrary Length and Scale

Chen, T., Tan, Z., Bo, X., Wu, Y., Gong, T., Chu, Q., & Ye, J. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i36.40280 · Cited by 1

BLM-Guard: Explainable Multimodal Ad Moderation with Chain-of-Thought and Policy-Aligned Rewards

Yang, Y., Liu, Z., Yuan, Y., Song, Y., Ma, X., Song, Y., Zeng, X., Sun, L., Wang, Y., Zhou, H., et al. (2026) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v40i42.40914 · Cited by 1

HAMSA: Hijacking Aligned Compact Models via Stealthy Automation

Krylov, A. S., Vagizov, I. I., Korzh, D., Douiba, M., Guezzaz, A., Kokh, V. N., Erokhin, S. D., Тутубалина, Е. В., & Rogov, O. Y. (2025) · Doklady Mathematics · doi:10.1134/s1064562425700425 · Cited by 3

T-pro 2.0: An Efficient Russian Hybrid-Reasoning Model and Playground

Stoianov, D., Taranets, D., Tsymboi, O., Latypov, R., Dautov, A., Kruglikov, V., Nikita, S., Abramov, G., Gein, P., Abulkhanov, D., et al. (2026) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2026.eacl-demo.22 · Cited by 1

Self-Alignment with Instruction BacktranslationPreprint

Li, X., Yu, P., Zhou, C., Schick, T., Levy, O., Zettlemoyer, L., Weston, J., & Lewis, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.06259 · Cited by 222

Chat Vector: A Simple Approach to Equip LLMs with Instruction Following and Model Alignment in New Languages

Huang, S.-C., Li, P.-Z., Hsu, Y.-C., Chen, K.-M., Lin, Y. T., Hsiao, S.-K., Tsai, R. T., & Lee, H.-Y. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.590 · Cited by 30

IHEval: Evaluating Language Models on Following the Instruction Hierarchy

Zhang, Z., Li, S., Zhang, Z., Liu, X., Jiang, H., Tang, X., Gao, Y., Li, Z., Wang, H., Tan, Z., et al. (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.425 · Cited by 22

Unified Pragmatic Models for Generating and Following Instructions

Fried, D., Andreas, J., & Klein, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1177 · Cited by 64

Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering

Si, S., Zhao, H., Chen, G., Gao, C., Bai, Y., Wang, Z., An, K., Luo, K., Chen, Q., Qi, F., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.804 · Cited by 17