---
title: "Jiaming Tang — author profile and papers"
description: "Jiaming Tang, Chongqing Normal University: 2 papers on Elamur Research (2024), h-index 9 — affiliations, topics, co-authors and who cites them in our…"
canonical: "https://research.elamur.ai/free/en/authors/jiaming-tang/"
---
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# Jiaming Tang

2 papers on this site · 2024

current affiliation [Chongqing Normal University](https://ror.org/01dcw5w74), Chongqing, China

Jiaming Tang has 2 papers on this site, in 2024, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/), [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Guangxuan Xiao](https://md.research.elamur.ai/free/en/authors/guangxuan-xiao/), [Song Han](https://md.research.elamur.ai/free/en/authors/song-han/), [Haotian Tang](https://md.research.elamur.ai/free/en/authors/haotian-tang/).

[OpenAlex](https://openalex.org/A5101184080) · [**h-index 9**](https://openalex.org/authors/A5101184080) · [13 works](https://openalex.org/works?filter=author.id:A5101184080) · [197 citations](https://openalex.org/works?filter=author.id:A5101184080&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[2 works on this site](https://md.research.elamur.ai/free/en/authors/jiaming-tang/#papers) · [303 citations in our library](https://md.research.elamur.ai/free/en/authors/jiaming-tang/cited-by/)

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

## Affiliations

- [Chongqing Normal University](https://ror.org/01dcw5w74) · Chongqing, China —current · 2021–2025 on 10 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

## Topics

[Language models & pretraining 2](https://md.research.elamur.ai/free/en/topics/language-models/) · [Information retrieval & search 1](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Efficient models & compression 1](https://md.research.elamur.ai/free/en/topics/efficient-models/)

## Works by year

### 2020s

[**2024** 2 papers](https://md.research.elamur.ai/free/en/authors/jiaming-tang/year/2024/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference](https://md.research.elamur.ai/free/en/papers/tang-2024-quest-query-aware-sparsity-efficient/)** *(2024 · 197 citations)* Generating text with very long contexts is slow because every new token requires reading the full KV cache from memory.
2. **[DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads](https://md.research.elamur.ai/free/en/papers/xiao-2024-duoattention-efficient-long-context-llm/)** *(2024 · 109 citations)* Serving LLMs over very long inputs is costly because the KV cache grows linearly with context length, and attention compute grows with it.

## Co-authors here

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

- [Guangxuan Xiao](https://md.research.elamur.ai/free/en/authors/guangxuan-xiao/) ×2
- [Song Han](https://md.research.elamur.ai/free/en/authors/song-han/) ×2
- [Haotian Tang](https://md.research.elamur.ai/free/en/authors/haotian-tang/)
- [Jingwei Zuo](https://md.research.elamur.ai/free/en/authors/jingwei-zuo/)
- [Yao Fu](https://md.research.elamur.ai/free/en/authors/yao-fu/)

## Cited by

Papers of our library cite their works 303 times (of the 140 884 papers whose bibliographies we read); [3 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/jiaming-tang/cited-by/).
