---
title: "Cayman Islands — research institutions, scientists and"
description: "Cayman Islands: 1 institutions with papers in our library in 0 agglomerations, 14 people with a page, 7 works, 1,809 citations."
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# Cayman Islands

Country 1 institution in 0 agglomerations · [all countries](https://md.research.elamur.ai/free/en/countries/)

[**1** institution with a page](https://md.research.elamur.ai/free/en/countries/cayman-islands/#institutions) · [**0** agglomerations](https://md.research.elamur.ai/free/en/countries/cayman-islands/#agglomerations) · [**14** people with a page](https://md.research.elamur.ai/free/en/countries/cayman-islands/#authors) · [**7** works in our library](https://md.research.elamur.ai/free/en/countries/cayman-islands/#works) · [**1,809** citations of their works](https://md.research.elamur.ai/free/en/countries/cayman-islands/#works)

## About

### Profile

The Cayman Islands appear on this site through a single research organisation: [Alibaba Group (Cayman Islands)](https://md.research.elamur.ai/free/en/companies/alibaba-group-cayman-islands/), a company based in George Town. No university, hospital or public laboratory from the islands has works here, and no urban agglomeration is defined for the country. Its scientific presence on this site is entirely industrial, and it is entirely about computing with language.

The works credited to the country fall almost wholly within [natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/), with one contribution also placed under [machine translation](https://md.research.elamur.ai/free/en/shelves/machine-translation/). Within that area the collection spans several distinct lines. These include fine-grained opinion mining, sentence and text representations for search, efficient adaptation of pretrained models, reasoning with large language models, and the reliability of systems that combine images and text. The country can be compared with others through the [list of all countries](https://md.research.elamur.ai/free/en/countries/). Its researchers and its single member organisation each have their own overview: people and institutions.

Because everything here comes from one company, the profile of the Cayman Islands on this site is really a profile of that company's research team as captured in the library. It does not describe the country's science more broadly.

### History

Among the works here, research tied to the Cayman Islands begins in 2020. Two papers from that year set out the early direction. One is a tagging method for extracting aspect-level opinions. The other is an unsupervised way of producing sentence embeddings. Together they show a team active both in structured sentiment analysis and in general-purpose text representation.

In 2021 both lines continued. The sentiment work moved from triplets to a richer four-part formulation, recast as a text generation problem. A study of adapter-based tuning broadened the representation and pretraining strand toward efficient model adaptation.

The works from 2023 mark a shift toward large models. One paper deals with chain-of-thought reasoning in a GPT-3-based system and how to make it more factual. Another addresses hallucinated objects in large vision-language models. The most recent work here, from 2024, returns to representation learning at a larger scale, with multilingual long-context encoders and rerankers for retrieval.

Read in order, the works trace a path from task-specific models built on pretrained encoders toward large generative models and multilingual retrieval systems. That path stays within one company and within the study of language throughout.

### Research profile

The clearest concentration on this site is in [sentiment and emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) analysis, specifically aspect-based sentiment. [Position-Aware Tagging for Aspect Sentiment Triplet Extraction](https://md.research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/) takes on the task of pulling out, from a sentence, which aspect is discussed, what opinion words describe it, and what polarity is expressed. Its approach encodes positional information directly in the tagging scheme. The follow-up, [Aspect Sentiment Quad Prediction as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/zhang-2021-aspect-sentiment-quad-prediction-as/), defines a task in which four sentiment elements must be produced together. It frames the output as a paraphrase that a generative model writes out. This connects the sentiment line to [text generation](https://md.research.elamur.ai/free/en/topics/text-generation/).

A second strand concerns [language models and pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) and how their internal representations can be reused. [An Unsupervised Sentence Embedding Method by Mutual Information Maximization](https://md.research.elamur.ai/free/en/papers/zhang-2020-unsupervised-sentence-embedding-method-mutual/) introduces IS-BERT, which learns sentence vectors without labelled examples by maximising mutual information between local and global views of a sentence. [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/) builds on the Houlsby adapter design. It examines when inserting small trainable modules into a frozen pretrained model serves as well as, or better than, full fine-tuning.

[Information retrieval and search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) together with [multilingual and cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/) are represented by [mGTE](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/). That work describes text encoders and reranking models for retrieval across many languages and over long inputs. Its encoder follows a BERT-like layout updated with rotary position embeddings, gated linear units and unpadding, and it reuses the XLM-R vocabulary.

[Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) appears through [Verify-and-Edit](https://md.research.elamur.ai/free/en/papers/zhao-2023-verify-edit-knowledge-enhanced-chain/). This framework takes the step-by-step reasoning chains a large language model produces, checks them against external knowledge, and edits them where they go wrong. The aim is more factual answers.

Finally, [vision and language](https://md.research.elamur.ai/free/en/topics/multimodal/) research is represented by [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://md.research.elamur.ai/free/en/papers/leng-2023-mitigating-object-hallucinations-large-vision/). The study observes that these models tend to describe objects that are not present, and that degrading the clarity of the input image makes this tendency stronger. It then contrasts the model's outputs on clean and distorted images during decoding to suppress the hallucinated content.

### Leading scientists

The researchers connected to the Cayman Islands on this site are all associated with Alibaba Group (Cayman Islands). They appear here in the order the country's overview gives them.

[Lidong Bing](https://md.research.elamur.ai/free/en/authors/lidong-bing/) comes first and has the broadest presence on the site among the country's researchers. Bing's papers fall within the company's language research, which spans sentiment analysis, representation learning and large-model reliability.

[Baosong Yang](https://md.research.elamur.ai/free/en/authors/baosong-yang/) follows. Yang's work belongs to the same natural language processing setting, and the country's machine translation and multilingual interests are the area closest to it.

[Jialong Tang](https://md.research.elamur.ai/free/en/authors/jialong-tang/) is next, again working within the company's language technology research.

[Pengjun Xie](https://md.research.elamur.ai/free/en/authors/pengjun-xie/) is another of the company's language researchers. The retrieval and text representation line, which culminates in the multilingual mGTE models, is the setting for that work.

[Nguyen Bach](https://md.research.elamur.ai/free/en/authors/nguyen-bach/) also contributes to the company's language research.

[Ruidan He](https://md.research.elamur.ai/free/en/authors/ruidan-he/) can be traced most directly through the works here. Ruidan He is the first author of the adapter-tuning study and a co-author of IS-BERT. That places this researcher at the centre of the strand on pretrained models and how they can be adapted or used to produce sentence embeddings without supervision.

[Ming Yan](https://md.research.elamur.ai/free/en/authors/ming-yan/) and [Yong Jiang](https://md.research.elamur.ai/free/en/authors/yong-jiang/) are next, both working within the company's language and multimodal research.

[Dingkun Long](https://md.research.elamur.ai/free/en/authors/dingkun-long/) is a co-author of mGTE, which ties Long's work here to multilingual, long-context retrieval and reranking.

[Haiyang Yu](https://md.research.elamur.ai/free/en/authors/haiyang-yu/) also belongs to the company's research team.

[Lu Xu](https://md.research.elamur.ai/free/en/authors/lu-xu/) is the first author of the position-aware tagging paper for aspect sentiment triplet extraction. That paper is the most cited of the country's works in our library, which places Xu at the start of the sentiment line.

[Qinghao Ye](https://md.research.elamur.ai/free/en/authors/qinghao-ye/) completes the group of named researchers. Ye works in the same industrial setting, where vision-language research forms one of the more recent directions.

### Landmark works

The most cited of the country's works here is the 2020 paper on position-aware tagging for aspect sentiment triplet extraction. It gave a single tagging scheme for jointly identifying aspects, opinion expressions and sentiment. Many later papers in the library cite it.

Close behind is the 2021 aspect sentiment quad prediction paper. It extended the problem to four elements and showed that generating a paraphrase-style sentence is a workable way to output them all at once. Taken together, these two papers make aspect-based sentiment the strongest single theme on this site for the country.

The 2023 study of visual contrastive decoding ranks next. It addresses a practical weakness of vision-language systems: describing objects that are not in the image. It offers a remedy that works at decoding time rather than by retraining the model.

The 2024 mGTE paper is the most recent of the major works. Among the country's works, it is also the one cited by the most papers within the library. Its contribution is a family of multilingual encoders and rerankers designed to handle long texts.

IS-BERT, from 2020, and the 2021 adapter study are both well cited here. Both belong to the period when pretrained encoders were being adapted to new uses. Verify-and-Edit, from 2023, rounds out the set with an approach to making chain-of-thought reasoning more faithful to external facts.

### Labs and units

The only organisation through which the Cayman Islands appear on this site is Alibaba Group (Cayman Islands), classed as a company and located in George Town. No separate departments, laboratories or subsidiary units are named for it here. Its research activity can be read only through the people and works attached to it.

### Collaborations

The author lists of the country's works show many co-authors beyond the company's named researchers. The aspect sentiment triplet paper names Lu Xu with Hao Li and Wei Lu. The quad prediction paper is led by Wenxuan Zhang, with Yang Deng and Xin Li among its authors. The visual contrastive decoding study is led by Sicong Leng, alongside Hang Zhang and Guanzheng Chen.

The mGTE work names Xin Zhang and Yanzhao Zhang together with Dingkun Long. IS-BERT is led by Yan Zhang, with Ruidan He and Zuozhu Liu. The adapter study pairs Ruidan He with Linlin Liu and Hai Ye. Verify-and-Edit is led by Ruochen Zhao, with Xingxuan Li and Shafiq Joty among its authors.

These author lists suggest that the company's research on this site is often carried out in teams with outside colleagues. However, no other institutions or countries are linked to the Cayman Islands here, so the partners' affiliations cannot be stated. Because the country has only one organisation on the site, there are no links between local universities and companies to describe.

### Timeline

In 2020 the country's research on this site begins with two works. One is position-aware tagging for aspect sentiment triplet extraction, which became the most cited of its papers here. The other is IS-BERT, an unsupervised sentence embedding method. Both build on pretrained encoders.

In 2021 the sentiment line advanced to aspect sentiment quad prediction framed as paraphrase generation. In the same year the adapter-tuning study examined lightweight alternatives to full fine-tuning of pretrained language models.

In 2023 the focus turned to large models. Verify-and-Edit offered a way to correct reasoning chains with external knowledge. Visual contrastive decoding tackled object hallucination in vision-language systems.

In 2024 mGTE brought the representation line to multilingual, long-context retrieval and reranking. It is the most recent of the country's works in our library.

## Where

## Agglomerations (0)

## Institutions (1)

### 🏢 Companies (1)

1. **[Alibaba Group (Cayman Islands)](https://md.research.elamur.ai/free/en/companies/alibaba-group-cayman-islands/)**Company · George Town, Cayman Islands*7 works · 1,809 citations*

## People (14)

Authors with a page confirmed at its institutions, the most cited first.

1. [**Lidong Bing**Alibaba Group (Cayman Islands)*13 papers here · 7,390 citations*](https://md.research.elamur.ai/free/en/authors/lidong-bing/)
2. [**Baosong Yang**Alibaba Group (Cayman Islands)*4 papers here · 4,577 citations*](https://md.research.elamur.ai/free/en/authors/baosong-yang/)
3. [**Jialong Tang**Alibaba Group (Cayman Islands)*2 papers here · 3,950 citations*](https://md.research.elamur.ai/free/en/authors/jialong-tang/)
4. [**Pengjun Xie**Alibaba Group (Cayman Islands)*4 papers here · 1,782 citations*](https://md.research.elamur.ai/free/en/authors/pengjun-xie/)
5. [**Nguyen Bach**Alibaba Group (Cayman Islands)*2 papers here · 1,669 citations*](https://md.research.elamur.ai/free/en/authors/nguyen-bach/)
6. [**Ruidan He**Alibaba Group (Cayman Islands)*3 papers here · 1,404 citations*](https://md.research.elamur.ai/free/en/authors/ruidan-he/)
7. [**Ming Yan**Alibaba Group (Cayman Islands)*2 papers here · 1,380 citations*](https://md.research.elamur.ai/free/en/authors/ming-yan/)
8. [**Yong Jiang**Alibaba Group (Cayman Islands)*2 papers here · 1,255 citations*](https://md.research.elamur.ai/free/en/authors/yong-jiang/)
9. [**Dingkun Long**Alibaba Group (Cayman Islands)*3 papers here · 927 citations*](https://md.research.elamur.ai/free/en/authors/dingkun-long/)
10. [**Haiyang Yu**Alibaba Group (Cayman Islands)*3 papers here · 907 citations*](https://md.research.elamur.ai/free/en/authors/haiyang-yu/)
11. [**Lu Xu**Alibaba Group (Cayman Islands)*2 papers here · 805 citations*](https://md.research.elamur.ai/free/en/authors/lu-xu/)
12. [**Qinghao Ye**Alibaba Group (Cayman Islands)*2 papers here · 762 citations*](https://md.research.elamur.ai/free/en/authors/qinghao-ye/)
13. [**Bosheng Ding**Alibaba Group (Cayman Islands)*2 papers here · 691 citations*](https://md.research.elamur.ai/free/en/authors/bosheng-ding/)
14. [**Linlin Liu**Alibaba Group (Cayman Islands)*2 papers here · 550 citations*](https://md.research.elamur.ai/free/en/authors/linlin-liu/)

## Works in our library (7)

Works of its institutions, the most cited first.

1. [**Position-Aware Tagging for Aspect Sentiment Triplet Extraction**Lu Xu, Hao Li, Wei Lu, et al.*2020 · 370 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-position-aware-tagging-aspect-sentiment/)
2. [**Aspect Sentiment Quad Prediction as Paraphrase Generation**Wenxuan Zhang, Yang Deng, Xin Li, et al.*2021 · 325 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2021-aspect-sentiment-quad-prediction-as/)
3. [**Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding**Sicong Leng, Hang Zhang, Guanzheng Chen, et al.*2023 · 317 citations*](https://md.research.elamur.ai/free/en/papers/leng-2023-mitigating-object-hallucinations-large-vision/)
4. [**mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/)
5. [**An Unsupervised Sentence Embedding Method by Mutual Information Maximization**Yan Zhang, Ruidan He, Zuozhu Liu, et al.*2020 · 213 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-unsupervised-sentence-embedding-method-mutual/)
6. [**On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation**Ruidan He, Linlin Liu, Hai Ye, et al.*2021 · 204 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/)
7. [**Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework**Ruochen Zhao, Xingxuan Li, Shafiq Joty, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-verify-edit-knowledge-enhanced-chain/)

## Concepts, shelves and topics

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

📚 Shelves 6[Computer Science · 7 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 6 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/)

🧭 Topics 7[Sentiment & emotion · 2 works](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Text generation · 1 work](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Vision & language · 1 work](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Information retrieval & search · 1 work](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Multilingual & cross-lingual NLP · 1 work](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/)

## Works by year

### 2020s

[**2024** 1 work](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/) · [**2023** 2 works](https://md.research.elamur.ai/free/en/countries/cayman-islands/year/2023/) · [**2021** 2 works](https://md.research.elamur.ai/free/en/countries/cayman-islands/year/2021/) · [**2020** 2 works](https://md.research.elamur.ai/free/en/countries/cayman-islands/year/2020/)

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Updated 23 October 2026