---
title: "Who cites Prafulla Dhariwal — page 5"
description: "12,963 papers of the Elamur Research library cite the works of Prafulla Dhariwal, found in their bibliographies · page 5 of 9 · 401–500; the most cited…"
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---
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# Who cites Prafulla Dhariwal

13,378 citations of their works from 12,963 papers of our library (of the 140,884 whose bibliographies we read) · 891 with a page here, the most cited first · page 5 of 9 · 401–500 · [← Prafulla Dhariwal](https://md.research.elamur.ai/free/en/authors/prafulla-dhariwal/)

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401. [**Making Language Models Better Reasoners with Step-Aware Verifier**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/)
402. [**Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models**Robert Logan IV, Ivana Balazevic, Eric Wallace, et al.*2022 · 215 citations*](https://md.research.elamur.ai/free/en/papers/iv-2022-cutting-down-prompts-parameters-simple/)
403. [**Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4**Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/)
404. [**GAIA: a benchmark for General AI Assistants**Gregoire Mialon, Clementine Fourrier, Craig Swift, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/)
405. [**MADLAD-400: A Multilingual And Document-Level Large Audited Dataset**Sneha Kudugunta, Isaac Caswell, Biao Zhang, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/)
406. [**An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/)
407. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
408. [**Teaching language models to support answers with verified quotes**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
409. [**Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/)
410. [**Reinforced Self-Training (ReST) for Language Modeling**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
411. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
412. [**Compressing Context to Enhance Inference Efficiency of Large Language Models**Yucheng Li, Bo Dong, Frank Guerin, et al.*2023 · 208 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-compressing-context-enhance-inference-efficiency/)
413. [**Sources of Hallucination by Large Language Models on Inference Tasks**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 208 citations*](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/)
414. [**Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation**Haoran Xu, Amr Sharaf, Yunmo Chen, et al.*2024 · 207 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/)
415. [**Progress measures for grokking via mechanistic interpretability**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/)
416. [**Ring Attention with Blockwise Transformers for Near-Infinite Context**Hao Liu, Matei Zaharia, Pieter Abbeel*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-ring-attention-blockwise-transformers-near/)
417. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/)
418. [**Active Example Selection for In-Context Learning**Yiming Zhang, Shi Feng, Chenhao Tan*2022 · 207 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/)
419. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
420. [**LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models**Yen-Ting Lin, Yun-Nung Chen*2023 · 206 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
421. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
422. [**Prompting GPT-3 To Be Reliable**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
423. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
424. [**Search-o1: Agentic Search-Enhanced Large Reasoning Models**Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.*2025 · 205 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/)
425. [**On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?**Nouha Dziri, Sivan Milton, Mo Yu, et al.*2022 · 205 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/)
426. [**RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 204 citations*](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/)
427. [**LLM+P: Empowering Large Language Models with Optimal Planning Proficiency**Bo Liu, Yuqian Jiang, Xiaohan Zhang, et al.*2023 · 204 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-llm-p-empowering-large-language/)
428. [**Internet-augmented language models through few-shot prompting for open-domain question answering**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/)
429. [**Towards a Unified Multi-Dimensional Evaluator for Text Generation**Ming Zhong, Yang Liu, Da Yin, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/)
430. [**On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation**Ruidan He, Linlin Liu, Hai Ye, et al.*2021 · 204 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/)
431. [**mGPT: Few-Shot Learners Go Multilingual**Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, et al.*2022 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shliazhko-2022-mgpt-few-shot-learners-go/)
432. [**Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 202 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/)
433. [**SALMONN: Towards Generic Hearing Abilities for Large Language Models**Changli Tang, Wenyi Yu, Guangzhi Sun, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-salmonn-towards-generic-hearing-abilities/)
434. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
435. [**Autoformalization with Large Language Models**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 202 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
436. [**DyLoRA: Parameter-Efficient Tuning of Pre-trained Models using Dynamic Search-Free Low-Rank Adaptation**Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, et al.*2023 · 201 citations*](https://md.research.elamur.ai/free/en/papers/valipour-2023-dylora-parameter-efficient-tuning-pre/)
437. [**Recursively Summarizing Books with Human Feedback**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 201 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/)
438. [**Aligning Large Language Models with Human: A Survey**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
439. [**CTRLsum: Towards Generic Controllable Text Summarization**Junxian He, Wojciech Kryscinski, Bryan McCann, et al.*2022 · 199 citations*](https://md.research.elamur.ai/free/en/papers/he-2022-ctrlsum-generic-controllable-text-summarization/)
440. [**Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset**Ke Wang, Junting Pan, Weikang Shi, et al.*2024 · 198 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-measuring-multimodal-mathematical-reasoning-math/)
441. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
442. [**Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/)
443. [**Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs**Suyu Ge, Yunan Zhang, Liyuan Liu, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-model-tells-discard-adaptive-kv/)
444. [**ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 197 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/)
445. [**Scalable Extraction of Training Data from (Production) Language Models**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 197 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/)
446. [**Improving Passage Retrieval with Zero-Shot Question Generation**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 197 citations*](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/)
447. [**The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning**Xi Ye, Greg Durrett*2022 · 196 citations*](https://md.research.elamur.ai/free/en/papers/ye-2022-unreliability-explanations-few-shot-prompting/)
448. [**InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models**Jinguo Zhu, Weiyun Wang, Zhe Chen, et al.*2025 · 195 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)
449. [**What matters when building vision-language models?**Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2024-matters-building-vision-language-models/)
450. [**InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction**Xiao Wang, Weikang Zhou, Can Zu, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/)
451. [**RRHF: Rank Responses to Align Language Models with Human Feedback without tears**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-rrhf-rank-responses-align-language/)
452. [**Making Retrieval-Augmented Language Models Robust to Irrelevant Context**Ori Yoran, Tomer Wolfson, Ori Ram, et al.*2023 · 193 citations*](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/)
453. [**OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models**Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.*2023 · 193 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/)
454. [**LLM in a flash: Efficient Large Language Model Inference with Limited Memory**Keivan Alizadeh, Seyed Iman Mirzadeh, Dmitry Belenko, et al.*2024 · 192 citations*](https://md.research.elamur.ai/free/en/papers/alizadeh-2024-llm-flash-efficient-large-language/)
455. [**MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning**Jun Chen, Deyao Zhu, Xiaoqian Shen, et al.*2023 · 192 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
456. [**Towards Making the Most of ChatGPT for Machine Translation**Keqin Peng, Liang Ding, Qihuang Zhong, et al.*2023 · 190 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-making-most-chatgpt-machine-translation/)
457. [**Challenges in Automated Debiasing for Toxic Language Detection**Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, et al.*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2021-challenges-automated-debiasing-toxic-language/)
458. [**GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer**Urchade Zaratiana, Nadi Tomeh, Pierre Holat, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/zaratiana-2024-gliner-generalist-model-named-entity/)
459. [**PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization**Yidong Wang, Zhuohao Yu, Zhengran Zeng, et al.*2023 · 189 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-pandalm-automatic-evaluation-benchmark-llm/)
460. [**When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models**Benjamin Muller, Antonios Anastasopoulos, Benoît Sagot, et al.*2021 · 189 citations*](https://md.research.elamur.ai/free/en/papers/muller-2021-when-being-unseen-mbert-is/)
461. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 187 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
462. [**SGPT: GPT Sentence Embeddings for Semantic Search**Niklas Muennighoff*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
463. [**Adaptive Machine Translation with Large Language Models**Yasmin Moslem, Rejwanul Haque, John D. Kelleher, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/moslem-2023-adaptive-machine-translation-large-language/)
464. [**RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models**Zekun Moore Wang, Zhongyuan Peng, Haoran Que, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-rolellm-benchmarking-eliciting-enhancing-role/)
465. [**SemEval-2023 Task 10: Explainable Detection of Online Sexism**Hannah Kirk, Wenjie Yin, Bertie Vidgen, et al.*2023 · 185 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-semeval-2023-task-10-explainable/)
466. [**Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models**Matt Deitke, Christopher Clark, Sangho Lee, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/)
467. [**OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents**Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-obelics-open-web-scale-filtered/)
468. [**Preference Ranking Optimization for Human Alignment**Feifan Song, Bowen Yu, Minghao Li, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-preference-ranking-optimization-human-alignment/)
469. [**Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/)
470. [**Re2G: Retrieve, Rerank, Generate**Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/)
471. [**Large Language Models are few(1)-shot Table Reasoners**Wenhu Chen*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/)
472. [**Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models**Yunfei Chu, Jin Xu, Xiaohuan Zhou, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-qwen-audio-advancing-universal/)
473. [**GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/)
474. [**Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning**Fuxiao Liu, Kevin Lin, Linjie Li, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/)
475. [**Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/)
476. [**Knowledge Conflicts for LLMs: A Survey**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 179 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/)
477. [**Adapting Language Models to Compress Contexts**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/)
478. [**CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/)
479. [**Eliciting Latent Predictions from Transformers with the Tuned Lens**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
480. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
481. [**On the Risk of Misinformation Pollution with Large Language Models**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/)
482. [**ToolQA: A Dataset for LLM Question Answering with External Tools**Yuchen Zhuang, Yue Yu, Kuan Wang, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-toolqa-dataset-llm-question-answering/)
483. [**8-bit Optimizers via Block-wise Quantization**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
484. [**RealTime QA: What's the Answer Right Now?**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/)
485. [**Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs**Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, et al.*2022 · 175 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-draft-sketch-prove-guiding-formal/)
486. [**Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models**Nora Kassner, Philipp Dufter, Hinrich Schütze*2021 · 175 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2021-multilingual-lama-investigating-knowledge-multilingual/)
487. [**DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/)
488. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 171 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
489. [**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
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