--- title: "Microsoft — works of 2024: 20 papers, the most cited first" description: "20 works of Microsoft from 2024 in our library, the most cited first. Among them: Phi-4 Technical Report arXiv:2412.08905." canonical: "https://research.elamur.ai/free/en/companies/microsoft/year/2024/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/companies/microsoft/year/2024/) [home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [companies](https://md.research.elamur.ai/free/en/companies/) / [Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) / 2024 # Works of Microsoft from 2024 20 works · the most cited first · [← Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/) 1. [**Phi-4 Technical Report arXiv:2412.08905**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/) 2. [**Large Language Models: A Survey arXiv:2402.06196**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 468 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/) 3. **[Improving Text Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/) [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.*2024 · 433 citations* 4. [**Multilingual E5 Text Embeddings: A Technical Report arXiv:2402.05672**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 343 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-multilingual-e5-text-embeddings-technical/) 5. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/) 6. [**EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty arXiv:2401.15077**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 204 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-speculative-sampling-requires-rethinking/) 7. **[Findings of the WMT24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet](https://md.research.elamur.ai/free/en/papers/kocmi-2024-findings-wmt24-general-machine-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, et al.*2024 · 177 citations* 8. **[Investigating Cultural Alignment of Large Language Models](https://md.research.elamur.ai/free/en/papers/alkhamissi-2024-investigating-cultural-alignment-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Badr AlKhamissi, Muhammad ElNokrashy, Mai Alkhamissi, et al.*2024 · 177 citations* 9. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 165 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/) 10. **[LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 159 citations* 11. [**LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens arXiv:2402.13753**Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, et al.*2024 · 139 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-longrope-extending-llm-context/) 12. **[EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://md.research.elamur.ai/free/en/papers/li-2024-eagle-2-faster-inference-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuhui Li, Fangyun Wei, Chao Zhang, et al.*2024 · 131 citations* 13. [**Orca-Math: Unlocking the potential of SLMs in Grade School Math arXiv:2402.14830**Arindam Mitra, Hamed Khanpour, Corby Rosset, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-orca-math-unlocking-potential-slms/) 14. **[Are LLMs Breaking MT Metrics? Results of the WMT24 Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/freitag-2024-are-llms-breaking-mt-metrics/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Markus Freitag, Nitika Mathur, Daniel Deutsch, et al.*2024 · 106 citations* 15. [**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models arXiv:2404.07738**Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, et al.*2024 · 106 citations*](https://md.research.elamur.ai/free/en/papers/baek-2024-researchagent-iterative-research-idea/) 16. **[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 · 103 citations* 17. **[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 · 92 citations* 18. **[Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-self-checker-plug-play-modules/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Miaoran Li, Baolin Peng, Michel Galley, et al.*2024 · 87 citations* 19. **[Chain-of-Dictionary Prompting Elicits Translation in Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2024-chain-dictionary-prompting-elicits-translation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hongyuan Lu, Haoran Yang, Haoyang Huang, et al.*2024 · 81 citations* 20. **[MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks](https://md.research.elamur.ai/free/en/papers/ahuja-2024-megaverse-benchmarking-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, et al.*2024 · 74 citations* - [← back to](https://md.research.elamur.ai/free/en/companies/microsoft/) — Microsoft