Core Insights - The article discusses the announcement of the ACL's new award for outstanding doctoral dissertations in computational linguistics, highlighting the significance of the award and its impact on the field of natural language processing [1][2][4]. Group 1: Award Details - The inaugural recipient of the ACL Doctoral Dissertation Award is Sewon Min from the University of Washington, recognized for her thesis titled "Rethinking Data Use in Large Language Models" [2][4]. - The award committee emphasized that Min's research provides critical insights into the behavior and capabilities of large language models, particularly in the area of in-context learning [4][14]. Group 2: Research Contributions - Min's dissertation discusses the understanding and advancement of large language models, focusing on their use of extensive training datasets [14]. - She demonstrates that the in-context learning ability of these models is largely determined by the content learned from training data [15]. - Min introduces a new class of language models called nonparametric language models, which utilize training data as a storage mechanism to retrieve information, enhancing accuracy and updatability [16][18]. Group 3: Other Nominated Works - The article also mentions three additional nominees for the award: Manling Li from the University of Illinois Urbana-Champaign, Ashish Sharma from the University of Washington, and Thomas Rishi Sherborne from the University of Edinburgh [8][20]. - Manling Li's work focuses on event-centric multimodal knowledge acquisition, proposing methods to transition from entity-centric to event-centric knowledge extraction [26][30]. - Ashish Sharma explores human-AI collaboration to improve mental health support, demonstrating how AI can enhance empathy in conversations and assist users in self-help interventions [45][51]. - Thomas Rishi Sherborne's research addresses cross-lingual transfer for semantic parsing, proposing strategies for effective adaptation of semantic parsers to new languages [62][64].