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Shiyu Chang

Shiyu Chang has 5 papers in our library, in 2018–2025, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Yang Zhang, Bairu Hou, Mo Yu.

5 papers in our library · 2018–2025

OpenAlex · ORCID · h-index 49 · 225 works · 8,710 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 380 citations in our library · 2 more in our library without a page yet · ⭐ Paper Star rank #2398

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

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 14 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.

 Works by year

2020s

2010s

 Papers

The most cited first (OpenAlex and our library).

  1. Diverse Few-Shot Text Classification with Multiple Metrics (2018 · 291 citations)
    Most few-shot learning research assumes that all tasks are drawn from one large, homogeneous collection, as with Omniglot or ImageNet.
  2. DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings (2022 · 269 citations)
    Unsupervised sentence embedding methods such as SimCSE train an encoder so that two dropout-perturbed copies of a sentence stay close together.
  3. Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control (2019 · 175 citations)
    Selective rationalization trains a generator to highlight a small part of the input text and a predictor that classifies using only that highlighted part. This paper identifies two weaknesses of the cooperative setup.
  4. A Survey on Data Selection for Language Models (2024 · 85 citations)
    Large language models depend on huge web-scale corpora, but using every available document is often wasteful or harmful because text quality varies widely.
  5. ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning (2025 · 64 citations)
    Reasoning models trained to produce long chains of thought often generate redundant, inefficient reasoning, which inflates inference cost.

Also in our library

2 more works of theirs are in our library without a page here yet; they will appear as we publish them.

 Co-authors here

12 of 23 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 380 times (of the 141,840 papers whose bibliographies we read); 25 citing papers with a page here →.

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