---
title: "Who cites Finetuned Language — page 3 — 1,887 citing papers"
description: "1,887 papers of the Elamur Research library cite «Finetuned Language Models Are Zero-Shot Learners», found in their bibliographies · page…"
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# Who cites «Finetuned Language Models Are Zero-Shot Learners»

1,887 papers of our library cite it (of the 149,439 whose bibliographies we read) · 499 with a page here, the most cited first · page 3 of 5 · 201–300 · [← back to «Finetuned Language Models Are Zero-Shot Learners»](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)

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201. **[Unlearn What You Want to Forget: Efficient Unlearning for LLMs](https://md.research.elamur.ai/free/en/papers/chen-2023-unlearn-what-you-want-forget/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaao Chen, Diyi Yang*2023 · 124 citations*
202. **[Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-mini-gemini-mining-potential-multi/) [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-pattern-analysis-and-machine-intelligence/)**Yanwei Li, Yuechen Zhang, Chengyao Wang, et al.*2024 · 123 citations*
203. [**Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training arXiv:2401.05566**Evan Hubinger, Carson Denison, Jesse Mu, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/hubinger-2024-sleeper-agents-training-deceptive-llms/)
204. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
205. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
206. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
207. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/)
208. [**#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language Models arXiv:2308.07074**Keming Lu, Hongyi Yuan, Zheng Yuan, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-instag-instruction-tagging-analyzing-supervised/)
209. [**UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition arXiv:2308.03279**Wenxuan Zhou, Sheng Zhang, Yu Gu, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-universalner-targeted-distillation-large-language/)
210. [**Explanations from Large Language Models Make Small Reasoners Better arXiv:2210.06726**Shiyang Li, Jianshu Chen, Yelong Shen, et al.*2022 · 120 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-explanations-large-language-models-make/)
211. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)
212. **[CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/wang-2024-charxiv-charting-gaps-realistic-chart/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zirui Wang, Mengzhou Xia, Luxi He, et al.*2024 · 119 citations*
213. [**SaulLM-7B: A pioneering Large Language Model for Law arXiv:2403.03883**Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/colombo-2024-saullm-7b-pioneering-large-language/)
214. [**Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering arXiv:2306.04136**Jinheon Baek, Alham Fikri Aji, Amir Saffari*2023 · 119 citations*](https://md.research.elamur.ai/free/en/papers/baek-2023-knowledge-augmented-language-model-prompting/)
215. **[Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation Extractors](https://md.research.elamur.ai/free/en/papers/zhang-2023-aligning-instruction-tasks-unlocks-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Zhang, Bernal Jimenez Gutierrez, Yu Su*2023 · 118 citations*
216. [**ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark arXiv:2303.13648**Haoran Wu, Wenxuan Wang, Yuxuan Wan, et al.*2023 · 118 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-chatgpt-grammarly-evaluating-chatgpt-grammatical/)
217. **[The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning](https://md.research.elamur.ai/free/en/papers/kim-2023-cot-collection-improving-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Seungone Kim, Se Joo, Doyoung Kim, et al.*2023 · 118 citations*
218. [**Platypus: Quick, Cheap, and Powerful Refinement of LLMs arXiv:2308.07317**Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-platypus-quick-cheap-powerful-refinement/)
219. [**PandaGPT: One Model To Instruction-Follow Them All arXiv:2305.16355**Yixuan Su, Tian Lan, Huayang Li, et al.*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/su-2023-pandagpt-one-model-instruction-follow/)
220. [**In-context Autoencoder for Context Compression in a Large Language Model arXiv:2307.06945**Tao Ge, Jing Hu, Lei Wang, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-context-autoencoder-context-compression-large/)
221. **[LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization](https://md.research.elamur.ai/free/en/papers/krishna-2023-longeval-guidelines-human-evaluation-faithfulness/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kalpesh Krishna, Erin Bransom, Bailey Kuehl, et al.*2023 · 114 citations*
222. [**ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning arXiv:2111.10952**Vamsi Aribandi, Yi Tay, Tal Schuster, et al.*2021 · 114 citations*](https://md.research.elamur.ai/free/en/papers/aribandi-2021-ext5-towards-extreme-multi-task/)
223. [**ALLaM: Large Language Models for Arabic and English arXiv:2407.15390**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
224. **[GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://md.research.elamur.ai/free/en/papers/khondaker-2023-gptaraeval-comprehensive-evaluation-chatgpt-arabic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi, et al.*2023 · 110 citations*
225. [**jina-embeddings-v3: Multilingual Embeddings With Task LoRA arXiv:2409.10173**Saba Sturua, Isabelle Mohr, Mohammad Kalim Akram, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/sturua-2024-jina-embeddings-v3-multilingual-embeddings/)
226. [**RouteLLM: Learning to Route LLMs with Preference Data arXiv:2406.18665**Isaac Ong, Amjad Almahairi, Vincent Wu, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ong-2024-routellm-learning-route-llms-preference/)
227. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/)
228. **[Learning to Retrieve In-Context Examples for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-learning-retrieve-context-examples-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Liang Wang, Nan Yang, Furu Wei*2024 · 106 citations*
229. **[Disentangling Length from Quality in Direct Preference Optimization](https://md.research.elamur.ai/free/en/papers/park-2024-disentangling-length-quality-direct-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ryan Park, Rafael Rafailov, Stefano Ermon, et al.*2024 · 105 citations*
230. [**LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions arXiv:2304.14402**Minghao Wu, Abdul Waheed, Chiyu Zhang, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/)
231. [**Statistical Rejection Sampling Improves Preference Optimization arXiv:2309.06657**Tianqi Liu, Yao Zhao, Rishabh Joshi, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-statistical-rejection-sampling-improves-preference/)
232. [**Zero-Shot Listwise Document Reranking with a Large Language Model arXiv:2305.02156**Xueguang Ma, Xinyu Zhang, Ronak Pradeep, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-zero-shot-listwise-document-reranking/)
233. [**PolyLM: An Open Source Polyglot Large Language Model arXiv:2307.06018**Xiangpeng Wei, Haoran Wei, Huan Lin, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-polylm-open-source-polyglot-large/)
234. [**FireAct: Toward Language Agent Fine-tuning arXiv:2310.05915**Baian Chen, Chang Shu, Ehsan Shareghi, et al.*2023 · 101 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-fireact-toward-language-agent-fine/)
235. [**Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security arXiv:2401.05459**Yuanchun Li, Hao Wen, Weijun Wang, et al.*2024 · 100 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-personal-llm-agents-insights-survey/)
236. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/)
237. [**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning arXiv:2310.03731**Ke Wang, Houxing Ren, Aojun Zhou, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/)
238. [**Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints arXiv:2212.05055**Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.*2022 · 98 citations*](https://md.research.elamur.ai/free/en/papers/komatsuzaki-2022-sparse-upcycling-training-mixture-experts/)
239. [**Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango arXiv:2209.07686**Aman Madaan, Amir Yazdanbakhsh*2022 · 98 citations*](https://md.research.elamur.ai/free/en/papers/madaan-2022-text-patterns-effective-chain-thought/)
240. [**Safety Alignment Should Be Made More Than Just a Few Tokens Deep arXiv:2406.05946**Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/qi-2024-safety-alignment-should-made-more/)
241. **[MAmmoTH2: Scaling Instructions from the Web](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 96 citations*
242. [**Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation arXiv:2305.15011**Haonan Li, Fajri Koto, Minghao Wu, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-bactrian-x-multilingual-replicable-instruction/)
243. **[True Few-Shot Learning with Prompts–-A Real-World Perspective](https://md.research.elamur.ai/free/en/papers/schick-2022-true-few-shot-learning-prompts/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Timo Schick, Hinrich Schütze*2022 · 96 citations*
244. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
245. **[LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/parmar-2024-logicbench-systematic-evaluation-logical-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mihir Parmar, Nisarg Patel, Neeraj Varshney, et al.*2024 · 93 citations*
246. **[Exploring the Potential of ChatGPT on Sentence Level Relations: A Focus on Temporal, Causal, and Discourse Relations](https://md.research.elamur.ai/free/en/papers/chan-2024-exploring-potential-chatgpt-sentence-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chunkit Chan, Cheng Jiayang, Weiqi Wang, et al.*2024 · 92 citations*
247. **[WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-wildteaming-scale-wild-jailbreaks-adversarially/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Liwei Jiang, Kavel Rao, Seungju Han, et al.*2024 · 92 citations*
248. [**ChatHaruhi: Reviving Anime Character in Reality via Large Language Model arXiv:2308.09597**Cheng Li, Ziang Leng, Chenxi Yan, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chatharuhi-reviving-anime-character-reality/)
249. [**Proving Test Set Contamination in Black Box Language Models arXiv:2310.17623**Yonatan Oren, Nicole Meister, Niladri Chatterji, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/oren-2023-proving-test-set-contamination-black/)
250. [**Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models arXiv:2306.08018**Yin Fang, Xiaozhuan Liang, Ningyu Zhang, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/fang-2023-mol-instructions-large-scale-biomolecular/)
251. **[Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://md.research.elamur.ai/free/en/papers/kumar-2023-language-generation-models-can-cause/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sachin Kumar, Vidhisha Balachandran, Lucille Njoo, et al.*2023 · 90 citations*
252. **[Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://md.research.elamur.ai/free/en/papers/rame-2023-rewarded-soups-towards-pareto-optimal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.*2023 · 90 citations*
253. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
254. **[GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://md.research.elamur.ai/free/en/papers/ghosh-2024-gama-large-audio-language-model/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sreyan Ghosh, Sonal Kumar, Ashish Seth, et al.*2024 · 87 citations*
255. [**SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal arXiv:2406.14598**Tinghao Xie, Xiangyu Qi, Yi Zeng, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/xie-2024-sorry-bench-systematically-evaluating-large/)
256. **[Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://md.research.elamur.ai/free/en/papers/zhang-2023-multilingual-large-language-models-are/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, et al.*2023 · 86 citations*
257. [**PromptNER: Prompting For Named Entity Recognition arXiv:2305.15444**Dhananjay Ashok, Zachary C. Lipton*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ashok-2023-promptner-prompting-named-entity-recognition/)
258. **[Solving Math Word Problems via Cooperative Reasoning induced Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2023-solving-math-word-problems-cooperative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Zhu, Junjie Wang, Lin Zhang, et al.*2023 · 86 citations*
259. **[The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*
260. [**Benchmark Data Contamination of Large Language Models: A Survey arXiv:2406.04244**Cheng Xu, Shuhao Guan, Derek Greene, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/)
261. [**MathScale: Scaling Instruction Tuning for Mathematical Reasoning arXiv:2403.02884**Zhengyang Tang, Xingxing Zhang, Benyou Wang, et al.*2024 · 83 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-mathscale-scaling-instruction-tuning-mathematical/)
262. **[LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://md.research.elamur.ai/free/en/papers/chalkidis-2023-lexfiles-legallama-facilitating-english-multinational/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Nicolas Garneau, Catalina Goanta, et al.*2023 · 83 citations*
263. **[Style Over Substance: Evaluation Biases for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-style-over-substance-evaluation-biases/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Minghao Wu, Alham Fikri Aji*2025 · 81 citations*
264. **[Interpretability at Scale: Identifying Causal Mechanisms in Alpaca](https://md.research.elamur.ai/free/en/papers/wu-2023-interpretability-scale-identifying-causal-mechanisms/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhengxuan Wu, Atticus Geiger, Thomas Icard, et al.*2023 · 81 citations*
265. **[Multilingual Instruction Tuning With Just a Pinch of Multilinguality](https://md.research.elamur.ai/free/en/papers/shaham-2024-multilingual-instruction-tuning-just-pinch/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Uri Shaham, Jonathan Herzig, Roee Aharoni, et al.*2024 · 78 citations*
266. [**FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research arXiv:2405.13576**Jiajie Jin, Yutao Zhu, Guanting Dong, et al.*2024 · 77 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-flashrag-modular-toolkit-efficient-retrieval/)
267. [**Evaluating GPT-4 and ChatGPT on Japanese Medical Licensing Examinations arXiv:2303.18027**Jungo Kasai, Yuhei Kasai, Keisuke Sakaguchi, et al.*2023 · 77 citations*](https://md.research.elamur.ai/free/en/papers/kasai-2023-evaluating-gpt-4-chatgpt-japanese/)
268. [**SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling arXiv:2312.15166**Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.*2023 · 77 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-solar-10-7b-scaling-large/)
269. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 76 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/)
270. **[Analyzing Dataset Annotation Quality Management in the Wild](https://md.research.elamur.ai/free/en/papers/klie-2024-analyzing-dataset-annotation-quality-management/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jan-Christoph Klie, Richard Eckart de Castilho, Iryna Gurevych*2024 · 76 citations*
271. [**GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking arXiv:2305.15066**Jiayan Guo, Lun Du, Hengyu Liu, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-gpt4graph-large-language-models-understand/)
272. [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration arXiv:2306.09093**Chenyang Lyu, Minghao Wu, Longyue Wang, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-macaw-llm-multi-modal-language/)
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