---
title: "Who cites Training language models to follow instructions…"
description: "5,770 papers of the Elamur Research library cite «Training language models to follow instructions with human feedback», found in their bibliographies ·…"
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# Who cites «Training language models to follow instructions with human feedback»

5,770 papers of our library cite it (of the 140,884 whose bibliographies we read) · 457 with a page here, the most cited first · page 5 of 5 · 401–457 · [← the paper](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)

[← prev](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/cited-by/page/4/) [1](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/cited-by/) … [4](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/cited-by/page/4/) **5**

401. [**Okay, Let's Do This! Modeling Event Coreference with Generated Rationales and Knowledge Distillation**Abhijnan Nath, Shadi Manafi Avari, Avyakta Chelle, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/nath-2024-okay-let-s-do-this/)
402. [**Rethinking Pragmatics in Large Language Models: Towards Open-Ended Evaluation and Preference Tuning**Shengguang Wu, Shusheng Yang, Zhenglun Chen, et al.*2024 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-rethinking-pragmatics-large-language-models/)
403. [**Evaluating Large Language Models for Health-related Queries with Presuppositions**Navreet Kaur, Monojit Choudhury, Danish Pruthi*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/kaur-2024-evaluating-large-language-models-health/)
404. [**Jais 2: A Family of Arabic-Centric Open Large Language Models**Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim, et al.*2026 · 14 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2026-jais-2-family-arabic-centric/)
405. [**Alignment at Pre-training! Towards Native Alignment for Arabic LLMs**Juhao Liang, Zhenyang Cai, Jianqing Zhu, et al.*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/liang-2024-alignment-pre-training-towards-native/)
406. [**Constituency Parsing using LLMs**Xuefeng Bai, Jialong Wu, Yulong Chen, et al.*2023 · 14 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-constituency-parsing-using-llms/)
407. [**from Benign import Toxic: Jailbreaking the Language Model via Adversarial Metaphors**Yu Yan, Sheng Sun, Zenghao Duan, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/yan-2025-benign-import-toxic-jailbreaking-language/)
408. [**Amharic LLaMA and LLaVA: Multimodal LLMs for Low Resource Languages**Michael Andersland*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/andersland-2024-amharic-llama-llava-multimodal-llms/)
409. [**Discourse Relation Prediction and Discourse Parsing in Dialogues with Minimal Supervision**Chuyuan Li, Chloé Braud, Maxime Amblard, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-discourse-relation-prediction-discourse-parsing/)
410. [**Pregnant Questions: The Importance of Pragmatic Awareness in Maternal Health Question Answering**Neha Srikanth, Rupak Sarkar, Heran Mane, et al.*2024 · 11 citations*](https://md.research.elamur.ai/free/en/papers/srikanth-2024-pregnant-questions-importance-pragmatic-awareness/)
411. [**SIGA: A Naturalistic NLI Dataset of English Scalar Implicatures with Gradable Adjectives**Rashid Nizamani, Sebastian Schuster, Vera Demberg*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/nizamani-2024-siga-naturalistic-nli-dataset-english/)
412. [**Bridging the Resource Gap: Exploring the Efficacy of English and Multilingual LLMs for Swedish**Oskar Holmström, Jenny Kunz, Marco Kuhlmann*2023 · 8 citations*](https://md.research.elamur.ai/free/en/papers/holmstrom-2023-bridging-resource-gap-exploring-efficacy/)
413. [**OSACT 2024 Task 2: Arabic Dialect to MSA Translation**Hanin Atwany, Nour Rabih, Ibrahim Mohammed, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/atwany-2024-osact-2024-task-2-arabic/)
414. [**RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework**Yifan Wang, Vera Demberg*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-rsa-control-pragmatics-grounded-lightweight/)
415. [**"Is the Pope Catholic?" Applying Chain-of-Thought Reasoning to Understanding Conversational Implicatures**Zae Myung Kim, David E. Taylor, Dongyeop Kang*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-pope-catholic-applying-chain-thought/)
416. [**GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning**Hasna Chouikhi, Manel Aloui, Cyrine Ben Hammou, et al.*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/chouikhi-2024-gemmar-enhancing-llms-through-arabic/)
417. [**IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection**Peter R\oysland Aarnes, Vinay Setty, Petra Galuščakova*2024 · 4 citations*](https://md.research.elamur.ai/free/en/papers/aarnes-2024-iai-group-checkthat-2024-transformer/)
418. [**When to generate hedges in peer-tutoring interactions**Alafate Abulimiti, Chloé Clavel, Justine Cassell*2023 · 4 citations*](https://md.research.elamur.ai/free/en/papers/abulimiti-2023-when-generate-hedges-peer-tutoring/)
419. [**Agent-Based Uncertainty Awareness Improves Automated Radiology Report Labeling with an Open-Source Large Language Model**Hadas Ben-Atya, Naama Gavrielov, Zvi Badash, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/ben-atya-2025-agent-based-uncertainty-awareness-improves/)
420. [**Prompting Instruction-tuned LLMs for Semantic Similarity Values**Xander Akiko Snelder, Yunchong Huang, Jelke Bloem*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/snelder-2026-prompting-instruction-tuned-llms-semantic/)
421. [**WASIL: In-the-Wild Arabic Spoken Interactions with LLMs**Zien Sheikh Ali, Hamdy Mubarak, Soon-Gyo Jung, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/ali-2026-wasil-wild-arabic-spoken-interactions/)
422. [**AlbanianLLMSafety: A Safety Evaluation Dataset for Large Language Models in Albanian**Wajdi Zaghouani, Kholoud Khalil Aldous, Isra Fejzullaj*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/zaghouani-2026-albanianllmsafety-safety-evaluation-dataset-large/)
423. [**Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety**Qian Shen, Fanghua Cao, Min Yao, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/shen-2026-children-s-english-reading-story/)
424. [**NUS-IDS at AMIYA/VarDial 2026: Improving Arabic Dialectness in LLMs with Reinforcement Learning**Sujatha Das Gollapalli, Mouad Hakam, Mingzhe Du, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/gollapalli-2026-nus-ids-amiya-vardial-2026/)
425. [**Reformulate and Create, Don't Translate: Creating Natural Prompts for Underserved Languages**Annika Simonsen, Mathias Stenlund, Lars Bungum, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/simonsen-2026-reformulate-create-don-t-translate/)
426. [**HAMSA: Hijacking Aligned Compact Models via Stealthy Automation**Alexey Krylov, Iskander Vagizov, Dmitrii Korzh, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/krylov-2025-hamsa-hijacking-aligned-compact-models/)
427. [**Deep Learning Based Code Generation Methods: Literature Review (基于深度学习的代码生成方法研究进展)**Zezhou Yang (杨泽洲), Sirong Chen (陈思榕), Cuiyun Gao (高翠芸), et al.*2023 · 1 citation*](https://md.research.elamur.ai/free/en/papers/yang-2023-deep-learning-code/)
428. [**A Binary Problem in Binary QA: Diverse LLMs or Diverse Question Interpretations? That Is the Ensembling Question**Rafael Rosales, Santiago Miret*2026*](https://md.research.elamur.ai/free/en/papers/rosales-2026-binary-problem-binary-qa-diverse/)
429. [**A Cascade Reinforcement Learning Strategy for Efficiently Enhancing the Reasoning Ability of Multimodal Large Language Models (高效提升多模态大语言模型推理能力的级联强化学习策略)**Weiyun Wang (王玮赟), Hengjun Pu (蒲恒骏), Linglin Jing (景凌林), et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-cascade-reinforcement-learning/)
430. [**A Single Model Ensemble Framework for Neural Machine Translation Using Pivot Translation**Seokjin Oh, Keonwoong Noh, Woohwan Jung*2026*](https://md.research.elamur.ai/free/en/papers/oh-2026-single-model-ensemble-framework-neural/)
431. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
432. [**Adaptive Emotion Management in Human-robot Dialogue using Online Group Relative Policy Optimization**Anna Manaseryan, Casey Kennington*2026*](https://md.research.elamur.ai/free/en/papers/manaseryan-2026-adaptive-emotion-management-human-robot/)
433. [**ArabicNumBench: Evaluating Arabic Number Reading in Large Language Models**Anas Alhumud, Abdulaziz Alhammadi, Muhammad Badruddin Khan*2026*](https://md.research.elamur.ai/free/en/papers/alhumud-2026-arabicnumbench-evaluating-arabic-number-reading/)
434. [**Are Social Biases in LLMs Consistent across Generative Tasks? A Case Study for Basque**Muitze Zulaika, Xabier Saralegi, Julia Shershneva, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zulaika-2026-are-social-biases-llms-consistent/)
435. [**Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models**Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia, et al.*2026*](https://md.research.elamur.ai/free/en/papers/altinisik-2026-cultural-knowledge-evaluating-arabic-cultural/)
436. [**Breaking the Benchmark: Revealing LLM Bias via Minimal Contextual Augmentation**Kaveh Eskandari Miandoab, Mahammed Kamruzzaman, Arshia Gharooni, et al.*2026*](https://md.research.elamur.ai/free/en/papers/miandoab-2026-breaking-benchmark-revealing-llm-bias/)
437. [**Breaking the Script: Do Role-Playing Agents Maintain Goal Alignment under Distraction?**Dongxu Lu, Albert Gatt, Johan Jeuring*2026*](https://md.research.elamur.ai/free/en/papers/lu-2026-breaking-script-do-role-playing/)
438. [**DeepICD-R1: Medical Reasoning through Hierarchical Rewards and Unsupervised Distillation**Tom Röhr, Thomas Maximilian Josef Steffek, Roman Teucher, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rohr-2026-deepicd-r1-medical-reasoning-through/)
439. [**DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents**Tianyi Zhang, Mousumi Das, Abrar Anwar, et al.*2026*](https://md.research.elamur.ai/free/en/papers/zhang-2026-dips-dialogue-policy-selection-high/)
440. [**Entity-Level Sentiment Analysis with Sentence Relevance Detection**Egil Rønningstad, Roman Klinger, Lilja Øvrelid, et al.*2026*](https://md.research.elamur.ai/free/en/papers/ronningstad-2026-entity-level-sentiment-analysis-sentence/)
441. [**Evaluation Drift in LLM Personality Induction: Are We Moving the Goalpost?**Prateek Kumar Rajput, Yewei Song, Iyiola Emmanuel Olatunji, et al.*2026*](https://md.research.elamur.ai/free/en/papers/rajput-2026-evaluation-drift-llm-personality-induction/)
442. [**HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model**Noam Kayzer, Dan Revital, Ori Bar Joseph, et al.*2026*](https://md.research.elamur.ai/free/en/papers/kayzer-2026-hebatron-hebrew-specialized-open-weight/)
443. [**InstructSum: A Benchmark to Evaluate Instruction-Following Capability of Large Language Models in Summarization**Kosuke Nishida, Kyosuke Nishida, Itsumi Saito*2026*](https://md.research.elamur.ai/free/en/papers/nishida-2026-instructsum-benchmark-evaluate-instruction-following/)
444. [**Multi-Agent Debate for Machine Translation: A Case Study on English-Japanese Translation**Zhan Shen, Jason Naradowsky, Xiaotian Wang, et al.*2026*](https://md.research.elamur.ai/free/en/papers/shen-2026-multi-agent-debate-machine-translation/)
445. [**NegNLI-BR: A Brazilian Portuguese Benchmark for Negation in Natural Language Inference**Matheus Westhelle, Viviane Moreira*2026*](https://md.research.elamur.ai/free/en/papers/westhelle-2026-negnli-br-brazilian-portuguese-benchmark/)
446. [**On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation**Weichuan Wang, Mingyang Liu, Chen Ma, et al.*2026*](https://md.research.elamur.ai/free/en/papers/wang-2026-temperature-constrained-non-deterministic-machine/)
447. [**Probing Discrete Speech Tokens of Spoken Language Models**Sven Naber, Julia Koch, Pranav Singh, et al.*2026*](https://md.research.elamur.ai/free/en/papers/naber-2026-probing-discrete-speech-tokens-spoken/)
448. [**SlovKE: A Large-Scale Dataset and LLM Evaluation for Slovak Keyphrase Extraction**Dávid Števaňák, Marek Suppa*2026*](https://md.research.elamur.ai/free/en/papers/stevanak-2026-slovke-large-scale-dataset-llm/)
449. [**Stands to Reason: Investigating the Effect of Reasoning on Idiomaticity Detection**Dylan Phelps, Rodrigo Wilkens, Edward Gow-Smith, et al.*2026*](https://md.research.elamur.ai/free/en/papers/phelps-2026-stands-reason-investigating-effect-reasoning/)
450. [**Synthetic Instruction Generation for Low-Resource Nordic Languages: Viability and Limitations in LLM Instruction-Tuning**Mathias Stenlund, Annika Simonsen, Lars Bungum, et al.*2026*](https://md.research.elamur.ai/free/en/papers/stenlund-2026-synthetic-instruction-generation-low-resource/)
451. [**Teaching LLMs to unveil tendentious implicit contents of Italian political communication**Walter Paci, Lorenzo Gregori, Alessandro Panunzi*2026*](https://md.research.elamur.ai/free/en/papers/paci-2026-teaching-llms-unveil-tendentious-implicit/)
452. [**ThinkStruct: RST-Aware Attention for Logical Reasoning in Machine Reading Comprehension**Nhi Thao Tran, Tien Le, Toan Pham, et al.*2026*](https://md.research.elamur.ai/free/en/papers/tran-2026-thinkstruct-rst-aware-attention-logical/)
453. [**Trustworthy NLP for Low-Resource Languages: Agent-Based Uncertainty Modeling for Hebrew Radiology Report Structuring**Hadas Ben Atya, Naama Gavrielov, Zvi Badash, et al.*2026*](https://md.research.elamur.ai/free/en/papers/atya-2026-trustworthy-nlp-low-resource-languages/)
454. [**An Empirical Study of Chinese Entity Linking Based on Large Language Model (基于大语言模型的中文实体链接实证研究)**Zhengfei Xu (徐正斐), Xin Xin (辛欣)*2025*](https://md.research.elamur.ai/free/en/papers/xu-2025-empirical-study-chinese/)
455. [**Huaputong: Large Language Model for Genealogical Question-answering with Knowledge Reasoning (华谱通: 基于知识推理的家谱问答大语言模型)**Xindong Wu (吴信东), Xingrui Zhuo (卓兴锐), Yongpan Chang (常永泮), et al.*2025*](https://md.research.elamur.ai/free/en/papers/wu-2025-huaputong-large-language/)
456. [**On the importance of Data Scale in Pretraining Arabic Language Models**Abbas Ghaddar, Philippe Langlais, Mehdi Rezagholizadeh, et al.*2024*](https://md.research.elamur.ai/free/en/papers/ghaddar-2024-importance-data-scale-pretraining-arabic/)
457. [**Questions After ChatGPT: Opportunities and Challenges of Very Large Pre-trained Models (问答ChatGPT之后: 超大预训练模型的机遇和挑战)**Jingwei Lu (卢经纬), Chao Guo (郭超), Xingyuan Dai (戴星原), et al.*2023*](https://md.research.elamur.ai/free/en/papers/lu-2023-questions-after-chatgpt/)

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