MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning (2025 · 15 citations)
Groups of LLMs that debate or discuss a problem often fail to improve with more rounds, because off-the-shelf models were never trained to cooperate.
SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents (2026 · 1 citation)
The paper looks at the safety of LLM search agents, which repeatedly write queries, retrieve documents, and reason over them.

All works

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

Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources ↗Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · no summary yetJoo-Kyung Kim et al.2017 · 183 citations
Intent detection using semantically enriched word embeddings ↗IEEE Spoken Language Technology Workshop (SLT) · no summary yetJoo-Kyung Kim et al.2016 · 120 citations
Deriving Adjectival Scales from Continuous Space Word Representations ↗Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · no summary yetJoo-Kyung Kim, Marie-Catherine de Marneffe2013 · 60 citations
MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement LearningAnnual Meeting of the Association for Computational Linguistics (ACL)Chanwoo Park, Seungju Han, Xingzhi Guo, et al.2025 · 15 citations
SafeSearch: Do Not Trade Safety for Utility in LLM Search AgentsFindings of the Association for Computational LinguisticsQiusi Zhan, Angeline Budiman-Chan, Abdelrahman Zayed, et al.2026 · 1 citation

Co-authors here

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

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

Papers of our library cite their works 113 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated 31 October 2026