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Siffi Singh

Siffi Singh has 2 papers in our library, in 2024–2026, most of them on Implicature, Language models & pretraining, English; often with Hang Su, Saab Mansour, Henry Peng Zou.

2 papers in our library · 2024–2026

OpenAlex · h-index 4 · 22 works · 49 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4 times in our library · ⭐ Paper Stars ranking: #10597

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

🏢AmazonSeattle · United States2023–2026 · 3 paperscurrent🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea2024 · 2 papers🎓The University of Texas at AustinAustin · United States2024 · 2 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.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.English · 1 workImplicature · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders (2024 · 4 citations)
    Intent embedding models (for chatbots that map «I want to order a pizza» to order_food) are tested on two things standard benchmarks miss: negation («I don't want to order pizza», a different intent with high word overlap) and implicature («I'm really hungry», the same intent with little overlap).
  2. Active Generalized Category Discovery with Diverse LLM Feedback (2026 · 1 citation)
    The paper addresses Generalized Category Discovery (GCD) for text. In GCD, a model must sort unlabeled data into both previously known and entirely new categories, using only a small labeled set drawn from the known ones. Earlier GCD approaches combine contrastive learning with K-Means++ clustering. Without extra…

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 4 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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