---
title: "Who cites Sam McCandlish"
description: "3,975 papers of the Elamur Research library cite the works of Sam McCandlish, found in their bibliographies · page 1 of 4 · 1–100; the most cited first."
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---
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# Who cites Sam McCandlish

4,762 citations of their works from 3,975 papers of our library (of the 140,884 whose bibliographies we read) · 331 with a page here, the most cited first · page 1 of 4 · 1–100 · [← Sam McCandlish](https://md.research.elamur.ai/free/en/authors/sam-mccandlish/)

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1. [**GPT-4 Technical Report**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 12,694 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
2. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,278 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
3. [**Language Models are Few-Shot Learners**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 10,782 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
4. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
5. [**Survey of Hallucination in Natural Language Generation**Ziwei Ji, Nayeon Lee, Rita Frieske, et al.*2022 · 5,947 citations*](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/)
6. [**LLaMA: Open and Efficient Foundation Language Models**Hugo Touvron, Thibaut Lavril, Gautier Izacard, et al.*2023 · 5,609 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/)
7. [**DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,593 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/)
8. [**Training Verifiers to Solve Math Word Problems**Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, et al.*2021 · 3,675 citations*](https://md.research.elamur.ai/free/en/papers/cobbe-2021-training-verifiers-solve-math-word/)
9. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
10. [**A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,221 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
11. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
12. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
13. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
14. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
15. [**Evaluating Large Language Models Trained on Code**Mark Chen, Jerry Tworek, Heewoo Jun, et al.*2021 · 2,529 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
16. [**Direct Preference Optimization: Your Language Model is Secretly a Reward Model**Rafael Rafailov, Archit Sharma, Eric Mitchell, et al.*2023 · 2,478 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/)
17. [**Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2022 · 2,416 citations*](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/)
18. [**Measuring Mathematical Problem Solving With the MATH Dataset**Dan Hendrycks, Collin Burns, Saurav Kadavath, et al.*2021 · 2,318 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/)
19. [**Emergent Abilities of Large Language Models**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,215 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
20. [**Scaling Instruction-Finetuned Language Models**Hyung Won Chung, Le Hou, Shayne Longpre, et al.*2022 · 2,176 citations*](https://md.research.elamur.ai/free/en/papers/chung-2022-scaling-instruction-finetuned-language-models/)
21. [**Flamingo: a Visual Language Model for Few-Shot Learning**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,108 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
22. [**TruthfulQA: Measuring How Models Mimic Human Falsehoods**Stephanie Lin, Jacob Hilton, Owain Evans*2022 · 2,047 citations*](https://md.research.elamur.ai/free/en/papers/lin-2022-truthfulqa-measuring-how-models-mimic/)
23. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,695 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
24. [**Retrieval-Augmented Generation for Large Language Models: A Survey**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,647 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
25. [**Qwen Technical Report**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,643 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
26. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
27. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
28. [**LAION-5B: An open large-scale dataset for training next generation image-text models**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,400 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
29. [**LaMDA: Language Models for Dialog Applications**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,314 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
30. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
31. [**The Rise and Potential of Large Language Model Based Agents: A Survey**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,262 citations*](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/)
32. [**Recipes for Building an Open-Domain Chatbot**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,206 citations*](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/)
33. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
34. [**Training Compute-Optimal Large Language Models**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/)
35. [**Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,094 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/)
36. [**OpenAI o1 System Card**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,065 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
37. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
38. [**FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/)
39. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 964 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
40. [**Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 962 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/)
41. [**Constitutional AI: Harmlessness from AI Feedback**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 957 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
42. [**Universal and Transferable Adversarial Attacks on Aligned Language Models**Andy Zou, Zifan Wang, Nicholas Carlini, et al.*2023 · 951 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/)
43. [**Extracting Training Data from Large Language Models**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 901 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
44. [**Segment Anything**Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al.*2023 · 865 citations*](https://md.research.elamur.ai/free/en/papers/kirillov-2023-segment-anything/)
45. [**GPT-NeoX-20B: An Open-Source Autoregressive Language Model**Sidney Black, Stella Biderman, Eric Hallahan, et al.*2022 · 818 citations*](https://md.research.elamur.ai/free/en/papers/black-2022-gpt-neox-20b-open-source/)
46. [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
47. [**Explainability for Large Language Models: A Survey**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 785 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/)
48. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 776 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
49. [**Reproducible scaling laws for contrastive language-image learning**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 750 citations*](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)
50. [**AdapterHub: A Framework for Adapting Transformers**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 747 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/)
51. [**Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 735 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/)
52. [**Capabilities of GPT-4 on Medical Challenge Problems**Harsha Nori, Nicholas King, Scott Mayer McKinney, et al.*2023 · 700 citations*](https://md.research.elamur.ai/free/en/papers/nori-2023-capabilities-gpt-4-medical-challenge/)
53. [**CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society**Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al.*2023 · 689 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/)
54. [**Large Language Models Are Human-Level Prompt Engineers**Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al.*2022 · 677 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/)
55. [**Jailbroken: How Does LLM Safety Training Fail?**Alexander Wei, Nika Haghtalab, Jacob Steinhardt*2023 · 659 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/)
56. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
57. [**HybridFlow: A Flexible and Efficient RLHF Framework**Guangming Sheng, Chi Zhang, Zilingfeng Ye, et al.*2024 · 598 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2024-hybridflow-flexible-efficient-rlhf-framework/)
58. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
59. [**s1: Simple test-time scaling**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/)
60. [**Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting**Miles Turpin, Julian Michael, Ethan Perez, et al.*2023 · 568 citations*](https://md.research.elamur.ai/free/en/papers/turpin-2023-language-models-don-t-always/)
61. [**Document Ranking with a Pretrained Sequence-to-Sequence Model**Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.*2020 · 538 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/)
62. [**Jailbreaking Black Box Large Language Models in Twenty Queries**Patrick Chao, Alexander Robey, Edgar Dobriban, et al.*2023 · 533 citations*](https://md.research.elamur.ai/free/en/papers/chao-2023-jailbreaking-black-box-large-language/)
63. [**LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs**Christoph Schuhmann, Richard Vencu, Romain Beaumont, et al.*2021 · 528 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2021-laion-400m-open-dataset-clip/)
64. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
65. [**Baichuan 2: Open Large-scale Language Models**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 512 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
66. [**OpenAssistant Conversations — Democratizing Large Language Model Alignment**Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/kopf-2023-openassistant-conversations-democratizing-large-language/)
67. [**Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis**Wenhao Zhu, Hongyi Liu, Qingxiu Dong, et al.*2024 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/)
68. [**Phi-4 Technical Report**Marah Abdin, Jyoti Aneja, Harkirat Behl, et al.*2024 · 489 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-4-technical-report/)
69. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
70. [**Inference-Time Intervention: Eliciting Truthful Answers from a Language Model**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 473 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/)
71. [**Large Language Models: A Survey**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 467 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
72. [**XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning**Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, et al.*2020 · 465 citations*](https://md.research.elamur.ai/free/en/papers/ponti-2020-xcopa-multilingual-dataset-causal-commonsense/)
73. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 456 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
74. [**OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems**Chaoqun He, Renjie Luo, Yuzhuo Bai, et al.*2024 · 448 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/)
75. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
76. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 431 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
77. [**AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 423 citations*](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/)
78. [**Security and Privacy Challenges of Large Language Models: A Survey**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 422 citations*](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/)
79. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 413 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
80. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
81. [**LongT5: Efficient Text-To-Text Transformer for Long Sequences**Mandy Guo, Joshua Ainslie, David Uthus, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/)
82. [**Large Language Models Cannot Self-Correct Reasoning Yet**Jie Huang, Xinyun Chen, Swaroop Mishra, et al.*2023 · 396 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/)
83. [**Whose Opinions Do Language Models Reflect?**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 390 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
84. [**Large Language Models as Optimizers**Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.*2023 · 388 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/)
85. [**Representation Engineering: A Top-Down Approach to AI Transparency**Andy Zou, Long Phan, Sarah Chen, et al.*2023 · 386 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/)
86. [**A Survey on Model Compression for Large Language Models**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/)
87. [**ChatDev: Communicative Agents for Software Development**Chen Qian, Wei Liu, Hongzhang Liu, et al.*2023 · 373 citations*](https://md.research.elamur.ai/free/en/papers/qian-2023-chatdev-communicative-agents-software/)
88. [**A Survey on Large Language Models for Code Generation**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
89. [**Towards Understanding Sycophancy in Language Models**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 354 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
90. [**BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/)
91. [**TinyLlama: An Open-Source Small Language Model**Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.*2024 · 350 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/)
92. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
93. [**Improving alignment of dialogue agents via targeted human judgements**Amelia Glaese, Nat McAleese, Maja Trębacz, et al.*2022 · 346 citations*](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/)
94. [**Toxicity in chatgpt: Analyzing persona-assigned language models**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 344 citations*](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
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