Daniel Scalena

Daniel Scalena is an academic researcher active in interdisciplinary domains spanning Computer Science and Arts and Humanities. His work focuses on Large Language Models (LLMs), Language Modeling, and Subjective Disagreement analysis, with a theoretical approach to model generation and intermediate representations.

• Key research areas: Computer Science (100%), Arts and Humanities (100%)
• Active in Language Modeling (100%), Model Generation (100%), and Subjective Analysis (100%)

Recent publications include:

• 2024: Conference contribution to BlackboxNLP Workshop on Multi-property Steering of Large Language Models (27 pages)
• 2023: Conference contribution to SemEval-2023 Task 11 on Subjective Disagreement (9 pages)

Collaborations include the Association for Computational Linguistics (ACL) and ACL Anthology.