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Stephen Rawls

Stephen Rawls has 2 papers in our library, in 2022–2025, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Reasoning; often with Andy Luo, Andy Rosenbaum, Anna Rumshisky.

2 papers in our library · 2022–2025

OpenAlex · ORCID · Wikidata · h-index 11 · 27 works · 783 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 60 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8073

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

🎓University of ChicagoChicago · United States2026 · 1 papercurrent🎓University of Southern CaliforniaLos Angeles · United States2016–2019 · 12 papers🏢AmazonSeattle · United States2020–2023 · 6 papers🏢Capital OneTysons Corner · United States2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution 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 4Research subjects and languages the work is about.Language models & pretraining · 2 worksMultilingual & cross-lingual NLP · 1 workBias, fairness & safety · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model (2022 · 84 citations)
    Large language models that learn from a few examples in the prompt have mostly been decoder-only. Their left-to-right attention can be a handicap when the input is long. This Amazon Alexa AI paper asks whether an encoder-decoder model can be an equally strong in-context learner.
  2. Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and Alignment (2025 · 9 citations)
    Conversational assistants built on LLMs need an input filter that catches fraudulent requests, illegal-activity requests and jailbreak attempts before they reach the main agent.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Pose-Aware Face Recognition in the Wild ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetIacopo Masi et al.2016 · 245 citations
  2. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelarXiv:2208.01448Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.2022 · 84 citations
  3. Refining Input Guardrails: Enhancing LLM-as-a-Judge Efficiency Through Chain-of-Thought Fine-Tuning and AlignmentarXiv:2501.13080Melissa Kazemi Rad, Huy Nghiem, Andy Luo, et al.2025 · 9 citations

 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 60 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/stephen-rawls/