🏢 Works of Microsoft from 2023

45 works · the most cited first · ← Microsoft

1. Improved Baselines with Visual Instruction TuningIEEE Computer Society Conference on Computer Vision and Pattern RecognitionHaotian Liu, Chunyuan Li, Yuheng Li, et al.2023 · 2,558 citations
2. Visual Instruction TuningarXiv:2304.08485Haotian Liu, Chunyuan Li, Qingyang Wu, et al.2023 · 2,491 citations
3. Sparks of Artificial General Intelligence: Early experiments with GPT-4arXiv:2303.12712Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al.2023 · 2,400 citations
4. Instruction Tuning with GPT-4arXiv:2304.03277Baolin Peng, Chunyuan Li, Pengcheng He, et al.2023 · 654 citations
5. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent ConversationarXiv:2308.08155Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.2023 · 584 citations
6. Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object DetectionarXiv:2303.05499Shilong Liu, Zhaoyang Zeng, Tianhe Ren, et al.2023 · 430 citations
7. Query Rewriting in Retrieval-Augmented Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Xinbei Ma, Yeyun Gong, Pengcheng He, et al.2023 · 383 citations
8. Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation SynergyarXiv:2305.15294Zhihong Shao, Yeyun Gong, Yelong Shen, et al.2023 · 381 citations
9. WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-InstructarXiv:2308.09583Haipeng Luo, Qingfeng Sun, Can Xu, et al.2023 · 366 citations
10. Orca: Progressive Learning from Complex Explanation Traces of GPT-4arXiv:2306.02707Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.2023 · 358 citations
11. Query2doc: Query Expansion with Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Liang Wang, Nan Yang, Furu Wei2023 · 353 citations
12. LLMLingua: Compressing Prompts for Accelerated Inference of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al.2023 · 335 citations
13. Neural Codec Language Models are Zero-Shot Text to Speech SynthesizersarXiv:2301.02111Chengyi Wang, Sanyuan Chen, Yu Wu, et al.2023 · 333 citations
14. MM-Vet: Evaluating Large Multimodal Models for Integrated CapabilitiesarXiv:2308.02490Weihao Yu, Zhengyuan Yang, Linjie Li, et al.2023 · 329 citations
15. Textbooks Are All You Need II: phi-1.5 technical reportarXiv:2309.05463Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, et al.2023 · 328 citations
16. CRITIC: Large Language Models Can Self-Correct with Tool-Interactive CritiquingarXiv:2305.11738Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.2023 · 315 citations
17. MathPrompter: Mathematical Reasoning using Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Shima Imani, Liang Du, Harsh Shrivastava2023 · 298 citations
18. OCRBench: On the Hidden Mystery of OCR in Large Multimodal ModelsScience China Information SciencesYuliang Liu, Zhang Li, Mingxin Huang, et al.2023 · 297 citations
19. Otter: A Multi-Modal Model with In-Context Instruction TuningIEEE Transactions on Pattern Analysis and Machine IntelligenceBo Li, Yuanhan Zhang, Liangyu Chen, et al.2023 · 280 citations
20. DSPy: Compiling Declarative Language Model Calls into Self-Improving PipelinesarXiv:2310.03714Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.2023 · 261 citations
21. DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language ModelsarXiv:2309.03883Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.2023 · 261 citations
22. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-OptimizersFindings of the Association for Computational LinguisticsDamai Dai, Yutao Sun, Li Dong, et al.2023 · 253 citations
23. ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem SolvingarXiv:2309.17452Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.2023 · 222 citations
24. Making Language Models Better Reasoners with Step-Aware VerifierAnnual Meeting of the Association for Computational Linguistics (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 Li2023 · 202 citations
26. Model Tells You What to Discard: Adaptive KV Cache Compression for LLMsarXiv:2310.01801Suyu Ge, Yunan Zhang, Liyuan Liu, et al.2023 · 198 citations
27. GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4Conference on Machine Translation (WMT)Tom Kocmi, Christian Federmann2023 · 191 citations
28. Towards Leaving No Indic Language Behind: Building Monolingual Corpora, Benchmark and Models for Indic LanguagesAnnual Meeting of the Association for Computational Linguistics (ACL)Sumanth Doddapaneni, Rahul Aralikatte, Gowtham Ramesh, et al.2023 · 174 citations
29. AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-TuningarXiv:2303.10512Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.2023 · 170 citations
30. Aligning Large Multimodal Models with Factually Augmented RLHFarXiv:2309.14525Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.2023 · 168 citations
31. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 160 citations
32. DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer ModelsarXiv:2309.14509Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, et al.2023 · 159 citations
33. LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt CompressionarXiv:2310.06839Huiqiang Jiang, Qianhui Wu, Xufang Luo, et al.2023 · 158 citations
34. AnnoLLM: Making Large Language Models to Be Better Crowdsourced AnnotatorsarXiv:2303.16854Xingwei He, Zhenghao Lin, Yeyun Gong, et al.2023 · 150 citations
35. Context-faithful Prompting for Large Language ModelsFindings of the Association for Computational LinguisticsWenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.2023 · 145 citations
36. MiniLLM: On-Policy Distillation of Large Language ModelsarXiv:2306.08543Yuxian Gu, Li Dong, Furu Wei, et al.2023 · 143 citations
37. Exploring the Limits of ChatGPT for Query or Aspect-based Text SummarizationarXiv:2302.08081Xianjun Yang, Yan Li, Xinlu Zhang, et al.2023 · 141 citations
38. Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human InterventionsAnnual Meeting of the Association for Computational Linguistics (ACL)John Chung, Ece Kamar, Saleema Amershi2023 · 141 citations
39. How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVENTransactions of the Association for Computational Linguistics (TACL)R. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.2023 · 116 citations
40. Large Language Models Understand and Can be Enhanced by Emotional StimuliarXiv:2307.11760Cheng Li, Jindong Wang, Yixuan Zhang, et al.2023 · 114 citations
41. Leveraging GPT-4 for Automatic Translation Post-EditingFindings of the Association for Computational LinguisticsVikas Raunak, Amr Sharaf, Yiren Wang, et al.2023 · 107 citations
42. SimLM: Pre-training with Representation Bottleneck for Dense Passage RetrievalAnnual Meeting of the Association for Computational Linguistics (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:2309.07462Rishav Hada, Varun Gumma, Adrian de Wynter, et al.2023 · 69 citations
44. Findings of the Second WMT Shared Task on Sign Language Translation (WMT-SLT23)Conference on Machine Translation (WMT)Mathias Müller, Malihe Alikhani, Eleftherios Avramidis, et al.2023 · 39 citations
45. Multilingual Coreference Resolution in Multiparty DialogueTransactions of the Association for Computational Linguistics (TACL)Boyuan Zheng, Patrick Xia, Mahsa Yarmohammadi, et al.2023 · 11 citations

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