---
title: "Luo Si · Alibaba DAMO Academy — author profile and papers"
description: "Luo Si, Alibaba DAMO Academy: 4 papers on Elamur Research (2020–2022), h-index 59 — affiliations, topics, co-authors and who cites…"
canonical: "https://research.elamur.ai/free/en/authors/luo-si/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/luo-si/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Luo Si

# Luo Si

Luo Si has 4 papers in our library, in 2020–2022, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Low-resource languages](https://md.research.elamur.ai/free/en/topics/low-resource-languages/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Lidong Bing](https://md.research.elamur.ai/free/en/authors/lidong-bing/), [Bosheng Ding](https://md.research.elamur.ai/free/en/authors/bosheng-ding/), [Linlin Liu](https://md.research.elamur.ai/free/en/authors/linlin-liu/).

4 papers in our library · 2020–2022

[OpenAlex](https://openalex.org/A5101718341) · [ORCID](https://orcid.org/0000-0002-3263-234X) · [Wikidata](https://www.wikidata.org/wiki/Q130881340) · [**h-index 59**](https://openalex.org/authors/A5101718341) · [338 works](https://openalex.org/works?filter=author.id:A5101718341) · [11,597 citations](https://openalex.org/works?filter=author.id:A5101718341&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 4 works in our library](https://md.research.elamur.ai/free/en/authors/luo-si/#papers) · [🔗 Cited 379 times in our library](https://md.research.elamur.ai/free/en/authors/luo-si/cited-by/) · [4 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #2998](https://md.research.elamur.ai/free/en/paper-stars/page/30/#star-2998)

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Luo+Si) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Luo+Si) · [dblp](https://dblp.org/search?q=Luo+Si) · [ACL Anthology](https://aclanthology.org/search/?q=Luo+Si)

## Affiliations

[🏢**Alibaba Group** Hangzhou · China 2017–2025 · 91 papers*current*](https://md.research.elamur.ai/free/en/companies/alibaba-group/) · [🏢**Alibaba DAMO Academy** Hangzhou · China 2020–2025 · 31 papers*current*](https://md.research.elamur.ai/free/en/companies/alibaba-damo-academy/) · [🎓**Purdue University West Lafayette** West Lafayette · United States 2006–2016 · 117 papers](https://md.research.elamur.ai/free/en/universities/purdue-university-west-lafayette/) · [🎓**Carnegie Mellon University** Pittsburgh · United States 2001–2012 · 29 papers](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) · [🏢**Alibaba Group** George Town · Cayman Islands 2020–2023 · 19 papers](https://md.research.elamur.ai/free/en/companies/alibaba-group-cayman-islands/) · [🏢**Alibaba Group** San Mateo · United States 2017–2020 · 8 papers](https://ror.org/00rn0m335) · [🎓**Nanyang Technological University** Singapore · Singapore 2021–2023 · 3 papers](https://md.research.elamur.ai/free/en/universities/nanyang-technological-university/) · [🏢**Salesforce** San Francisco · United States 2023 · 2 papersEmployment](https://md.research.elamur.ai/free/en/companies/salesforce/) · [🎓**Beihang University** Beijing · China 2007–2009 · 2 papers](https://md.research.elamur.ai/free/en/universities/beihang-university/) · [🎓**Tsinghua University** Beijing · China 2006–2007 · 2 papers](https://md.research.elamur.ai/free/en/universities/tsinghua-university/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

## Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7 The field of science this work belongs to (OpenAlex fields).[Arts and Humanities · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Computer Science · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 4 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 4 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Biochemistry, Genetics and Molecular Biology · 1 work](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · [Molecular Biology · 1 work](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/)

🧭 Topics 3 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Low-resource languages · 1 work](https://md.research.elamur.ai/free/en/topics/low-resource-languages/) · [Evaluation & benchmarks · 1 work](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/)

## Works by year

### 2020s

[**2022** 1 paper RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://md.research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/) · [**2021** 2 papers Luo Si's papers of 2021](https://md.research.elamur.ai/free/en/authors/luo-si/year/2021/) · [**2020** 1 paper DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks](https://md.research.elamur.ai/free/en/papers/ding-2020-daga-data-augmentation-generation-approach/)

## Papers

The most cited first (OpenAlex and our library).

1. **[On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/)** *(2021 · 204 citations)* Adapter-based tuning inserts small trainable modules into a frozen pretrained language model (PrLM). Earlier work valued it mainly for saving parameters. This paper asks a different question: when does it actually beat full fine-tuning? The authors use the Houlsby et al. adapter design. Each transformer layer gets…
2. **[DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks](https://md.research.elamur.ai/free/en/papers/ding-2020-daga-data-augmentation-generation-approach/)** *(2020 · 203 citations)* Sequence tagging tasks label every token, so they are easily hurt by noisy augmented data. Common augmentation strategies also depend on resources that low-resource languages often lack, such as WordNet, bilingual corpora or in-domain weak taggers. DAGA addresses this by generating new labeled sentences from scratch.
3. **[RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://md.research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/)** *(2022 · 141 citations)* Most relation extraction systems need large annotated datasets and only handle a fixed set of relation types.
4. **[CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark](https://md.research.elamur.ai/free/en/papers/zhang-2021-cblue-chinese-biomedical-language/)** *(2021 · 71 citations)* Biomedical NLP evaluation resources such as BLURB and PubMedQA are almost entirely English. Chinese, spoken by roughly a quarter of humanity, has had no shared yardstick for medical language understanding. The authors address this with CBLUE, which they describe as the first Chinese benchmark of its kind. It combines…

## All works

8 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

1. **[On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruidan He, Linlin Liu, Hai Ye, et al.*2021 · 204 citations*
2. **[DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks](https://md.research.elamur.ai/free/en/papers/ding-2020-daga-data-augmentation-generation-approach/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bosheng Ding, Linlin Liu, Lidong Bing, et al.*2020 · 203 citations*
3. **[StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding](https://arxiv.org/abs/1908.04577) arXiv:1908.04577 · original ↗ · no summary yet**Wei Wang et al.*2019 · 174 citations*
4. **[RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://md.research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yew Ken Chia, Lidong Bing, Soujanya Poria, et al.*2022 · 141 citations*
5. **["Bilingual Expert" Can Find Translation Errors](https://arxiv.org/abs/1807.09433) arXiv:1807.09433 · original ↗ · no summary yet**Kai Fan et al.*2018 · 82 citations*
6. [**CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark arXiv:2106.08087**Ningyu Zhang, Mosha Chen, Zhen Bi, et al.*2021 · 71 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2021-cblue-chinese-biomedical-language/)
7. **[Alibaba Submission for WMT18 Quality Estimation Task](https://aclanthology.org/W18-6465/) Proceedings of the Third Conference on Machine Translation: Shared Task Papers · original ↗ · no summary yet**Jiayi Wang et al.*2018 · 57 citations*
8. **[Competency-Aware Neural Machine Translation: Can Machine Translation Know its Own Translation Quality?](https://aclanthology.org/2022.emnlp-main.330/) Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yet**Pei Zhang et al.*2022 · 3 citations*

## Co-authors here

12 of 16 co-authors with a page, the most papers together first.

- [Lidong Bing](https://md.research.elamur.ai/free/en/authors/lidong-bing/) ×3
- [Bosheng Ding](https://md.research.elamur.ai/free/en/authors/bosheng-ding/) ×2
- [Linlin Liu](https://md.research.elamur.ai/free/en/authors/linlin-liu/) ×2
- [Canasai Kruengkrai](https://md.research.elamur.ai/free/en/authors/canasai-kruengkrai/)
- [Chuanqi Tan](https://md.research.elamur.ai/free/en/authors/chuanqi-tan/)
- [Chunyan Miao](https://md.research.elamur.ai/free/en/authors/chunyan-miao/)
- [Fei Huang](https://md.research.elamur.ai/free/en/authors/fei-huang/)
- [Lei Li](https://md.research.elamur.ai/free/en/authors/lei-li/)
- [Liying Cheng](https://md.research.elamur.ai/free/en/authors/liying-cheng/)
- [Ningyu Zhang](https://md.research.elamur.ai/free/en/authors/ningyu-zhang/)
- [Qingyu Tan](https://md.research.elamur.ai/free/en/authors/qingyu-tan/)
- [Ruidan He](https://md.research.elamur.ai/free/en/authors/ruidan-he/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ×2: [EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/): [ACL 2021](https://md.research.elamur.ai/free/en/venues/acl/2021/)
- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2022](https://md.research.elamur.ai/free/en/venues/findings/2022/)
- Proceedings of the Third Conference on Machine Translation: Shared Task Papers

## Cited by

Papers of our library cite their works 379 times (of the 141,840 papers whose bibliographies we read); [22 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/luo-si/cited-by/).

Updated 24 October 2026