---
title: "Junlan Feng · China Mobile — author profile and papers"
description: "Junlan Feng, China Mobile: 6 papers on Elamur Research (2010–2026), h-index 26 — affiliations, topics, co-authors and who cites them in…"
canonical: "https://research.elamur.ai/free/en/authors/junlan-feng/"
---
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# Junlan Feng

Junlan Feng has 6 papers in our library, in 2010–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/), [Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/); often with [Xue Han](https://md.research.elamur.ai/free/en/authors/xue-han/), [Lijun Mei](https://md.research.elamur.ai/free/en/authors/lijun-mei/), [Qian Hu](https://md.research.elamur.ai/free/en/authors/qian-hu/).

6 papers in our library · 2010–2026

[OpenAlex](https://openalex.org/A5079750750) · [ORCID](https://orcid.org/0000-0001-5292-2945) · [Wikidata](https://www.wikidata.org/wiki/Q130934138) · [**h-index 26**](https://openalex.org/authors/A5079750750) · [204 works](https://openalex.org/works?filter=author.id:A5079750750) · [2,586 citations](https://openalex.org/works?filter=author.id:A5079750750&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/junlan-feng/#papers) · [🔗 Cited 91 times in our library](https://md.research.elamur.ai/free/en/authors/junlan-feng/cited-by/) · [2 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #2570](https://md.research.elamur.ai/free/en/paper-stars/page/26/#star-2570)

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=Junlan+Feng) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Junlan+Feng) · [dblp](https://dblp.org/search?q=Junlan+Feng) · [ACL Anthology](https://aclanthology.org/search/?q=Junlan+Feng)

## Affiliations

[🏢**China Mobile Research Institute** Beijing · China 2016–2026 · 112 papers*current*](https://md.research.elamur.ai/free/en/companies/china-mobile-research-institute/) · [🏢**China Mobile** Beijing · China 2016–2026 · 31 papers*current*](https://md.research.elamur.ai/free/en/companies/china-mobile/) · [🏢**AT&T** Bedminster · United States 2004–2021 · 20 papers](https://md.research.elamur.ai/free/en/companies/at-t/) · [🎓**Tsinghua University** Beijing · China 2021–2024 · 12 papers](https://md.research.elamur.ai/free/en/universities/tsinghua-university/) · [🎓**Nanjing University** Nanjing · China 2024–2025 · 2 papers](https://md.research.elamur.ai/free/en/universities/nanjing-university/) · [🏥**Shanghai Tenth People's Hospital** Shanghai · China 2020–2021 · 2 papers](https://ror.org/03vjkf643) · [🎓**Tongji University** Shanghai · China 2020–2021 · 2 papers](https://md.research.elamur.ai/free/en/universities/tongji-university/) · [🏛️**Chinese Academy of Sciences** Beijing · China 2002–2016 · 2 papers](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) · [🎓**Shanghai Jiao Tong University** Shanghai · China 2014–2015 · 2 papers](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) · [🏥**Shanghai Sixth People's Hospital** Shanghai · China 2014–2015 · 2 papers](https://ror.org/049zrh188)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 more institutions on single bylines are not listed.

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Experimental and Cognitive Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Psychology · 1 work](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Arts and Humanities · 5 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 5 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Decision Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Management Science and Operations Research · 1 work](https://md.research.elamur.ai/free/en/shelves/decision-sciences/management-science-and-operations-research/)

🧭 Topics 6 Research subjects and languages the work is about.[Sentiment & emotion · 1 work](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · [Language models & pretraining · 3 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Code & programming languages · 1 work](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · [Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Machine translation · 1 work](https://md.research.elamur.ai/free/en/topics/machine-translation/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/)

## Works by year

### 2020s

[**2026** 5 papers](https://md.research.elamur.ai/free/en/authors/junlan-feng/year/2026/)

### 2010s

[**2010** 1 paper](https://md.research.elamur.ai/free/en/authors/junlan-feng/year/2010/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Robust Sentiment Detection on Twitter from Biased and Noisy Data](https://md.research.elamur.ai/free/en/papers/barbosa-2010-robust-sentiment-detection-twitter-biased/)** *(2010 · 955 citations)* Barbosa and Feng address sentiment classification of tweets. Tweets are too short for the classic n-gram models built for long texts such as reviews and news, and hand-labelled training data is expensive. They use a two-stage pipeline: first separate subjective from objective tweets, then label subjective ones as…
2. **[Thinking Alignment of Scenario-Oriented User Simulation](https://md.research.elamur.ai/free/en/papers/wu-2026-thinking-alignment-scenario-oriented-user/)** *(2026 · 1 citation)* LLM-based user simulators, whether they prompt a model to role-play or fine-tune it on real user turns, usually reproduce only what users type.
3. **[AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/luo-2026-alphaqt-bench-diagnosing-gap-between/)** *(2026)* LLMs increasingly write executable Python code for alpha mining rather than symbolic factor formulas. This adds expressiveness but also hidden risks: look-ahead bias (using future data) and bugs in stateful logic. Existing alpha benchmarks mostly score backtest profitability, which can reward leaked information or…
4. **[ChildEval:WHEN LARGE LANGUAGE MODELS MEET CHILDREN'S PERSONALITIES](https://md.research.elamur.ai/free/en/papers/luo-2026-childeval-when-large-language-models/)** *(2026)* LLM-based chatbots are increasingly used as companions for young children. Yet nobody has systematically tested whether these models pick up on and respect a child's preferences. Existing personalization benchmarks mostly target adults, and child-focused ones cover only narrow aspects such as safety or style…
5. **[PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shen-2026-pegrl-improving-machine-translation-post/)** *(2026)* LLM translation trained with reinforcement learning (e.g., GRPO) suffers from two problems. Its Monte Carlo group baselines are noisy, and its huge output space rewards broad exploration but gives little signal for small local fixes. PEGRL addresses this by treating translation as a two-step workflow: a draft…
6. **[Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-soft-orthogonal-low-rank-adaptation/)** *(2026)* The paper addresses continual learning (CL) for large language models. Full retraining is too costly at this scale, and most existing CL methods focus only on preventing catastrophic forgetting, without exploiting knowledge that tasks could share. The authors first observe that better-performing CL models produce…

## All works

8 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

1. **[Robust Sentiment Detection on Twitter from Biased and Noisy Data](https://md.research.elamur.ai/free/en/papers/barbosa-2010-robust-sentiment-detection-twitter-biased/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Luciano Barbosa, Junlan Feng*2010 · 955 citations*
2. **[Thinking Alignment of Scenario-Oriented User Simulation](https://md.research.elamur.ai/free/en/papers/wu-2026-thinking-alignment-scenario-oriented-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoting Wu, Yi Huang, Chunyang Gao, et al.*2026 · 1 citation*
3. **[AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs](https://md.research.elamur.ai/free/en/papers/luo-2026-alphaqt-bench-diagnosing-gap-between/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sichun Luo, Yi Huang, Shichang Meng, et al.*2026*
4. **[ChildEval:WHEN LARGE LANGUAGE MODELS MEET CHILDREN'S PERSONALITIES](https://md.research.elamur.ai/free/en/papers/luo-2026-childeval-when-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yanyan Luo, Xue Han, Chunxu Zhao, et al.*2026*
5. **[PEGRL: Improving Machine Translation by Post-Editing Guided Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shen-2026-pegrl-improving-machine-translation-post/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yunzhi Shen, Hao Zhou, Xin Huang, et al.*2026*
6. **[Soft Orthogonal Low-Rank Adaptation for Knowledge Sharing in Large Language Model Continual Learning](https://md.research.elamur.ai/free/en/papers/wang-2026-soft-orthogonal-low-rank-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yitong Wang, Xue Han, WenChun Gao, et al.*2026*
7. **[D2PCM:A Multi-Turn Dialogue Dataset with Personalized Contextual Memory ↗](https://aclanthology.org/2026.findings-acl.1870/) Findings of the Association for Computational Linguistics: ACL 2026 · no summary yet**Zhe Yang et al.*2026*
8. **[A Learnable Skill Combination Strategy for Multi-task Learning in Natural Language Understanding ↗](https://aclanthology.org/2026.findings-acl.1886/) Findings of the Association for Computational Linguistics: ACL 2026 · no summary yet**Zhe Yang et al.*2026*

## Co-authors here

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

- [Xue Han](https://md.research.elamur.ai/free/en/authors/xue-han/) ×3
- [Lijun Mei](https://md.research.elamur.ai/free/en/authors/lijun-mei/) ×2
- [Qian Hu](https://md.research.elamur.ai/free/en/authors/qian-hu/) ×2
- [Yi Huang](https://md.research.elamur.ai/free/en/authors/yi-huang/) ×2
- [Hao Zhou](https://md.research.elamur.ai/free/en/authors/hao-zhou/)
- [Mukai Li](https://md.research.elamur.ai/free/en/authors/mukai-li/)
- [Qi Liu](https://md.research.elamur.ai/free/en/authors/qi-liu/)
- [Shujian Huang](https://md.research.elamur.ai/free/en/authors/shujian-huang/)
- [Sichun Luo](https://md.research.elamur.ai/free/en/authors/sichun-luo/)
- [Xin Huang](https://md.research.elamur.ai/free/en/authors/xin-huang/)
- [Yitong Wang](https://md.research.elamur.ai/free/en/authors/yitong-wang/)
- [Ziqing Wang](https://md.research.elamur.ai/free/en/authors/ziqing-wang/)

## Venues

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

- [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/) ×3: [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ×3
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/) ×2
- Findings of the Association for Computational Linguistics: ACL 2026 ×2
- [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/): [COLING 2010](https://md.research.elamur.ai/free/en/venues/coling/2010/)

## Cited by

Papers of our library cite their works 91 times (of the 149,439 papers whose bibliographies we read); [14 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/junlan-feng/cited-by/).

Updated 1 November 2026