Elamur Research⌕
☰
home / authors / Carolin Lawrence

Carolin Lawrence

Carolin Lawrence has 4 papers in our library, in 2018–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Semantic parsing & meaning representation; often with Kiril Gashteovski, Graham Neubig, Vijay Viswanathan.

4 papers in our library · 2018–2026

OpenAlex · h-index 10 · 93 works · 372 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 107 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #5585

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

🏢NECHeidelberg · Germany2021–2026 · 29 paperscurrent🎓Ss. Cyril and Methodius University in SkopjeSkopje · North Macedonia2025–2026 · 3 paperscurrent🎓Heidelberg UniversityHeidelberg · Germany2016–2021 · 7 papers🎓University of WashingtonSeattle · United States2024–2025 · 2 papers🏢NECTokyo · Japan2022 · 2 papers

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

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksGeneral Social Sciences · 1 workSocial Sciences · 1 workDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksSemantic parsing & meaning representation · 1 workEvaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models Enable Few-Shot Clustering (2024 · 111 citations)
    Clustering without supervision is underspecified, because the algorithm cannot know which grouping the user actually wants.
  2. Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (2018 · 68 citations)
    Building training data for semantic parsers is costly because expert-written parses are hard to obtain. Gold answers are also hard to define for fuzzy geographical questions over OpenStreetMap (OSM), such as "near" or walking-distance queries. Lawrence and Riezler explore a weaker signal. A deployed parser proposes a…
  3. Evaluating Language Models as Synthetic Data Generators (2025 · 32 citations)
    Synthetic data now drives much of language model post-training. Earlier data-generation studies, however, each used different generator LMs, data volumes, student models and benchmarks, so their results cannot be compared to show which LM produces the most useful training data. Kim et al. address this with…
  4. Compositional Steering of Large Language Models with Steering Tokens (2026)
    Practical LLM deployments often need outputs that satisfy several requirements at once, such as a target language, length and format.

All works

8 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Large Language Models Enable Few-Shot ClusteringTransactions of the Association for Computational Linguistics (TACL)Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, et al.2024 · 111 citations
  2. Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit FeedbackAnnual Meeting of the Association for Computational Linguistics (ACL)Carolin Lawrence, Stefan Riezler2018 · 68 citations
  3. Evaluating Language Models as Synthetic Data GeneratorsAnnual Meeting of the Association for Computational Linguistics (ACL)Seungone Kim, Juyoung Suk, Xiang Yue, et al.2025 · 32 citations
  4. Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · no summary yetEmmy Liu et al.2025 · 6 citations
  5. KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · no summary yetHaris Widjaja et al.2022 · 6 citations
  6. On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists ↗arXiv:2605.20668 · no summary yetSeungone Kim et al.2026 · 2 citations
  7. Compositional Steering of Large Language Models with Steering TokensAnnual Meeting of the Association for Computational Linguistics (ACL)Gorjan Radevski, Kiril Gashteovski, Giwon Hong, et al.2026
  8. Scaling Evaluation-Time Compute with Reasoning Models as Evaluators ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetSeungone Kim et al.2026

 Co-authors here

12 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 107 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

↑ back to top

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