Arto Hellas
Computer Science Applications · 2018–2023
A researcher in the Science corpus: 9 papers; trusts 12 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computer Science Applications — 74 citations into it; #347 of 1,855 by trust here, #381 by citations
- Information Systems — 61 citations into it; #2,996 of 8,771 by trust here, #2,307 by citations
- Human-Computer Interaction — 41 citations into it; #1,307 of 2,253 by trust here, #1,161 by citations
- Software — 32 citations into it; #806 of 1,227 by trust here, #439 by citations
- Code Generation & Program Synthesis — 26 citations into it; #134 of 220 by trust here, #98 by citations
- Developmental and Educational Psychology — 24 citations into it
- Large Language Models — 23 citations into it; #411 of 799 by trust here, #576 by citations
- Software Engineering — 23 citations into it; #72 of 113 by trust here, #65 by citations
Whom Arto Hellas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Paul Denny
- Nea Pirttinen
- Juho Leinonen
- February Friday
- Kirsti Ala-Mutka
- Sami Sarsa
- Raymond Lister
- Stephen MacNeil
- Andrew Luxton-Reilly
- Petri Ihantola
Who trusts Arto Hellas overall
Papers from Arto Hellas's perspective
Ordered by how strongly the researchers Arto Hellas trusts endorse each paper. Same corpus, a different order for every researcher.
- Evaluating Large Language Models Trained on Code (arXiv.org)
- Hierarchical Text-Conditional Image Generation with CLIP Latents (arXiv.org)
- Program Synthesis with Large Language Models
- Extracting Training Data from Large Language Models
- On the Opportunities and Risks of Foundation Models
- Scaling Language Models: Methods, Analysis & Insights from Training Gopher
- Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm
- Training Verifiers to Solve Math Word Problems
- Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
- Competition-level code generation with AlphaCode
Their papers
- Comparing Code Explanations Created by Students and Large Language Models (2023) — 209 citations
- Computing Education in the Era of Generative AI (2023) — 325 citations
- Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models (2022) — 502 citations
- Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book (2022) — 251 citations
- Robosourcing Educational Resources - Leveraging Large Language Models for Learnersourcing (2022) — 40 citations
- Using Large Language Models to Enhance Programming Error Messages (2022) — 245 citations
- Crowdsourcing Content Creation for SQL Practice (2020) — 20 citations
- Experimenting with Model Solutions as a Support Mechanism (2019) — 5 citations
- Crowdsourcing programming assignments with CrowdSorcerer (2018) — 28 citations