---
title: "Microsoft — works of 2023: 45 papers, the most cited first"
description: "45 works of Microsoft from 2023 in our library, the most cited first. Among them: Improved Baselines with Visual Instruction Tuning IEEE…"
canonical: "https://research.elamur.ai/free/en/companies/microsoft/year/2023/"
---
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# Works of Microsoft from 2023

45 works · the most cited first · [← Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/)

1. **[Improved Baselines with Visual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,558 citations*
2. [**Visual Instruction Tuning arXiv:2304.08485**Haotian Liu, Chunyuan Li, Qingyang Wu, et al.*2023 · 2,491 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/)
3. [**Sparks of Artificial General Intelligence: Early experiments with GPT-4 arXiv:2303.12712**Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.*2023 · 2,400 citations*](https://md.research.elamur.ai/free/en/papers/bubeck-2023-sparks-artificial-general-intelligence-early/)
4. [**Instruction Tuning with GPT-4 arXiv:2304.03277**Baolin Peng, Chunyuan Li, Pengcheng He, et al.*2023 · 654 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-instruction-tuning-gpt-4/)
5. [**AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation arXiv:2308.08155**Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.*2023 · 584 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/)
6. [**Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection arXiv:2303.05499**Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al.*2023 · 430 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-grounding-dino-marrying-dino-grounded/)
7. **[Query Rewriting in Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 383 citations*
8. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy arXiv:2305.15294**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 381 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
9. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct arXiv:2308.09583**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 366 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
10. [**Orca: Progressive Learning from Complex Explanation Traces of GPT-4 arXiv:2306.02707**Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.*2023 · 358 citations*](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/)
11. **[Query2doc: Query Expansion with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-query2doc-query-expansion-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Liang Wang, Nan Yang, Furu Wei*2023 · 353 citations*
12. **[LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2023-llmlingua-compressing-prompts-accelerated-inference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al.*2023 · 335 citations*
13. [**Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers arXiv:2301.02111**Chengyi Wang, Sanyuan Chen, Yu Wu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-neural-codec-language-models-zero/)
14. [**MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities arXiv:2308.02490**Weihao Yu, Zhengyuan Yang, Linjie Li, et al.*2023 · 329 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/)
15. [**Textbooks Are All You Need II: phi-1.5 technical report arXiv:2309.05463**Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, et al.*2023 · 328 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-textbooks-all-need-ii-phi/)
16. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing arXiv:2305.11738**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
17. **[MathPrompter: Mathematical Reasoning using Large Language Models](https://md.research.elamur.ai/free/en/papers/imani-2023-mathprompter-mathematical-reasoning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shima Imani, Liang Du, Harsh Shrivastava*2023 · 298 citations*
18. **[OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2023-ocrbench-hidden-mystery-ocr-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Yuliang Liu, Zhang Li, Mingxin Huang, et al.*2023 · 297 citations*
19. **[Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://md.research.elamur.ai/free/en/papers/li-2023-otter-multi-modal-model-context/) [IEEE Transactions on Pattern Analysis and Machine Intelligence](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-pattern-analysis-and-machine-intelligence/)**Bo Li, Yuanhan Zhang, Liangyu Chen, et al.*2023 · 280 citations*
20. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines arXiv:2310.03714**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
21. [**DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models arXiv:2309.03883**Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
22. **[Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 253 citations*
23. [**ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving arXiv:2309.17452**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 222 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-tora-tool-integrated-reasoning-agent/)
24. **[Making Language Models Better Reasoners with Step-Aware Verifier](https://md.research.elamur.ai/free/en/papers/li-2023-making-language-models-better-reasoners/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifei Li, Zeqi Lin, Shizhuo Zhang, et al.*2023 · 216 citations*
25. [**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/)
26. [**Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs arXiv:2310.01801**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/)
27. **[GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4](https://md.research.elamur.ai/free/en/papers/kocmi-2023-gemba-mqm-detecting-translation-quality/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Tom Kocmi, Christian Federmann*2023 · 191 citations*
28. **[Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic Languages](https://md.research.elamur.ai/free/en/papers/doddapaneni-2023-leaving-no-indic-language-behind/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, et al.*2023 · 174 citations*
29. [**AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning arXiv:2303.10512**Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/)
30. [**Aligning Large Multimodal Models with Factually Augmented RLHF arXiv:2309.14525**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/)
31. [**Orca 2: Teaching Small Language Models How to Reason arXiv:2311.11045**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/)
32. [**DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models arXiv:2309.14509**Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, et al.*2023 · 159 citations*](https://md.research.elamur.ai/free/en/papers/jacobs-2023-deepspeed-ulysses-system-optimizations-enabling/)
33. [**LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression arXiv:2310.06839**Huiqiang Jiang, Qianhui Wu, Xufang Luo, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-longllmlingua-accelerating-enhancing-llms-long/)
34. [**AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators arXiv:2303.16854**Xingwei He, Zhenghao Lin, Yeyun Gong, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-annollm-making-large-language-models/)
35. **[Context-faithful Prompting for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 145 citations*
36. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
37. [**Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization arXiv:2302.08081**Xianjun Yang, Yan Li, Xinlu Zhang, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-exploring-limits-chatgpt-query-aspect/)
38. **[Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions](https://md.research.elamur.ai/free/en/papers/chung-2023-increasing-diversity-while-maintaining-accuracy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Chung, Ece Kamar, Saleema Amershi*2023 · 141 citations*
39. **[How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN](https://md.research.elamur.ai/free/en/papers/mccoy-2023-how-much-do-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**R. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.*2023 · 116 citations*
40. [**Large Language Models Understand and Can be Enhanced by Emotional Stimuli arXiv:2307.11760**Cheng Li, Jindong Wang, Yixuan Zhang, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-understand-enhanced/)
41. **[Leveraging GPT-4 for Automatic Translation Post-Editing](https://md.research.elamur.ai/free/en/papers/raunak-2023-leveraging-gpt-4-automatic-translation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vikas Raunak, Amr Sharaf, Yiren Wang, et al.*2023 · 107 citations*
42. **[SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval](https://md.research.elamur.ai/free/en/papers/wang-2023-simlm-pre-training-representation-bottleneck/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2023 · 106 citations*
43. [**Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? arXiv:2309.07462**Rishav Hada, Varun Gumma, Adrian de Wynter, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/hada-2023-large-language-model-based-evaluators/)
44. **[Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)](https://md.research.elamur.ai/free/en/papers/muller-2023-findings-second-wmt-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Mathias Müller, Malihe Alikhani, Eleftherios Avramidis, et al.*2023 · 39 citations*
45. **[Multilingual Coreference Resolution in Multiparty Dialogue](https://md.research.elamur.ai/free/en/papers/zheng-2023-multilingual-coreference-resolution-multiparty-dialogue/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, et al.*2023 · 11 citations*
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