---
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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---
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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 3 of 5 · 201–300 · [← the paper](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)

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201. [**How Language Model Hallucinations Can Snowball**Muru Zhang, Ofir Press, William Merrill, et al.*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/)
202. [**MedAlpaca — An Open-Source Collection of Medical Conversational AI Models and Training Data**Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, et al.*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/)
203. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
204. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 219 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
205. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 219 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
206. [**Self-critiquing models for assisting human evaluators**William Saunders, Catherine Yeh, Jeff Wu, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/saunders-2022-self-critiquing-models-assisting-human/)
207. [**Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models**Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/)
208. [**AlpaGasus: Training A Better Alpaca with Fewer Data**Lichang Chen, Shiyang Li, Jun Yan, et al.*2023 · 214 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-alpagasus-training-better-alpaca-fewer/)
209. [**WildChat: 1M ChatGPT Interaction Logs in the Wild**Wenting Zhao, Xiang Ren, Jack Hessel, et al.*2024 · 213 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-wildchat-1m-chatgpt-interaction-logs/)
210. [**Fine-Grained Human Feedback Gives Better Rewards for Language Model Training**Zeqiu Wu, Yushi Hu, Weijia Shi, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/)
211. [**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/)
212. [**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/)
213. [**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/)
214. [**LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders**Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.*2024 · 209 citations*](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/)
215. [**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/)
216. [**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/)
217. [**PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/)
218. [**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/)
219. [**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/)
220. [**Exploring Human-Like Translation Strategy with Large Language Models**Zhiwei He, Tian Liang, Wenxiang Jiao, et al.*2024 · 207 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-exploring-human-like-translation-strategy/)
221. [**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/)
222. [**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/)
223. [**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/)
224. [**SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization**Hyunwoo Kim, Jack Hessel, Liwei Jiang, et al.*2023 · 201 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-soda-million-scale-dialogue-distillation/)
225. [**IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages**Jay Gala, Pranjal A. Chitale, Raghavan AK, et al.*2023 · 200 citations*](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/)
226. [**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/)
227. [**Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2**Hamish Ivison, Yizhong Wang, Valentina Pyatkin, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/)
228. [**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/)
229. [**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/)
230. [**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/)
231. [**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/)
232. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
233. [**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/)
234. [**Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/)
235. [**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/)
236. [**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/)
237. [**One Embedder, Any Task: Instruction-Finetuned Text Embeddings**Hongjin Su, Weijia Shi, Jungo Kasai, et al.*2022 · 191 citations*](https://md.research.elamur.ai/free/en/papers/su-2022-one-embedder-any-task-instruction/)
238. [**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/)
239. [**Refusal in Language Models Is Mediated by a Single Direction**Andy Arditi, Oscar Obeso, Aaquib Syed, et al.*2024 · 188 citations*](https://md.research.elamur.ai/free/en/papers/arditi-2024-refusal-language-models-mediated-single/)
240. [**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/)
241. [**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/)
242. [**LawBench: Benchmarking Legal Knowledge of Large Language Models**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 184 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
243. [**Multilingual Jailbreak Challenges in Large Language Models**Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-multilingual-jailbreak-challenges-large-language/)
244. [**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/)
245. [**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/)
246. [**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/)
247. [**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/)
248. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 180 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/)
249. [**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/)
250. [**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/)
251. [**On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/)
252. [**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/)
253. [**Effective Long-Context Scaling of Foundation Models**Wenhan Xiong, Jingyu Liu, Igor Molybog, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-effective-long-context-scaling-foundation/)
254. [**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/)
255. [**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/)
256. [**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/)
257. [**What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning**Wei Liu, Weihao Zeng, Keqing He, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-makes-good-data-alignment-comprehensive/)
258. [**Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs**Shengbang Tong, Ellis Brown, Penghao Wu, et al.*2024 · 173 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-cambrian-1-fully-open-vision/)
259. [**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/)
260. [**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/)
261. [**OpenChat: Advancing Open-source Language Models with Mixed-Quality Data**Guan Wang, Sijie Cheng, Xianyuan Zhan, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-openchat-advancing-open-source-language/)
262. [**Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs**Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/balloccu-2024-leak-cheat-repeat-data-contamination/)
263. [**LESS: Selecting Influential Data for Targeted Instruction Tuning**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/)
264. [**HuaTuo: Tuning LLaMA Model with Chinese Medical Knowledge**Haochun Wang, Chi Liu, Nuwa Xi, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-huatuo-tuning-llama-model-chinese/)
265. [**Learning to Compress Prompts with Gist Tokens**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/)
266. [**WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models**Hongliang He, Wenlin Yao, Kaixin Ma, et al.*2024 · 168 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-webvoyager-building-end-end-web/)
267. [**Aligning Large Multimodal Models with Factually Augmented RLHF**Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/)
268. [**Low-Resource Languages Jailbreak GPT-4**Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/)
269. [**Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization**Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiante Brantley, et al.*2022 · 166 citations*](https://md.research.elamur.ai/free/en/papers/ramamurthy-2022-reinforcement-learning-natural-language-processing/)
270. [**Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts**Jian Xie, Kai Zhang, Jiangjie Chen, et al.*2023 · 165 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/)
271. [**Large Language Model Unlearning**Yuanshun Yao, Xiaojun Xu, Yang Liu*2023 · 165 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-large-language-model-unlearning/)
272. [**Large Language Diffusion Models**Shen Nie, Fengqi Zhu, Zebin You, et al.*2025 · 164 citations*](https://md.research.elamur.ai/free/en/papers/nie-2025-large-language-diffusion-models/)
273. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/)
274. [**Quark: Controllable Text Generation with Reinforced Unlearning**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 164 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/)
275. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
276. [**Improve Mathematical Reasoning in Language Models by Automated Process Supervision**Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-improve-mathematical-reasoning-language-models/)
277. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
278. [**Long-form factuality in large language models**Jerry Wei, Chengrun Yang, Xinying Song, et al.*2024 · 162 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/)
279. [**Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation**Yangsibo Huang, Samyak Gupta, Mengzhou Xia, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/)
280. [**GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts**Jiahao Yu, Xingwei Lin, Zheng Yu, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-gptfuzzer-red-teaming-large-language/)
281. [**LooGLE: Can Long-Context Language Models Understand Long Contexts?**Jiaqi Li, Mengmeng Wang, Zilong Zheng, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-loogle-long-context-language-models/)
282. [**Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents**Zihao Wang, Shaofei Cai, Guanzhou Chen, et al.*2023 · 161 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-describe-explain-plan-select-interactive/)
283. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
284. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
285. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
286. [**AgentTuning: Enabling Generalized Agent Abilities for LLMs**Aohan Zeng, Mingdao Liu, Rui Lu, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2023-agenttuning-enabling-generalized-agent/)
287. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/)
288. [**From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning**Ming Li, Yong Zhang, Zhitao Li, et al.*2023 · 157 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-quantity-quality-boosting-llm-performance/)
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