Najoung Kim
· Assistant ProfessorNew York University · Center for Data Science
Active 2016–2025
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About
Najoung Kim is an Assistant Professor at Boston University and a faculty fellow at the NYU Center for Data Science. Her research focuses on advancing the understanding and development of artificial intelligence and data science, with an emphasis on interdisciplinary applications. She has contributed to the field through original research that fosters collaborations across disciplines, working towards innovative solutions in AI and data science.
Research topics
- Artificial Intelligence
- Computer Science
- Natural Language Processing
- Programming language
- Data Mining
- Linguistics
- Philosophy
- Physics
- Mathematics
Selected publications
arXiv (Cornell University) · 2023-07-05 · 18 citations
preprintOpen accessThe impressive performance of recent language models across a wide range of tasks suggests that they possess a degree of abstract reasoning skills. Are these skills general and transferable, or specialized to specific tasks seen during pretraining? To disentangle these effects, we propose an evaluation framework based on "counterfactual" task variants that deviate from the default assumptions underlying standard tasks. Across a suite of 11 tasks, we observe nontrivial performance on the counterf…
CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists
2025-01-01 · 2 citations
articleOpen accessSenior authorExisting LLM-as-a-Judge approaches for evaluating text generation suffer from rating inconsistencies, with low agreement and high rating variance across different evaluator models.We attribute this to subjective evaluation criteria combined with Likert scale scoring in existing protocols.To address this issue, we introduce CheckEval, a checklist-based evaluation framework that improves rating reliability via decomposed binary questions.Through experiments with 12 evaluator models across multiple…
Personas as a Way to Model Truthfulness in Language Models
2024-01-01 · 2 citations
articleOpen accessLarge language models (LLMs) are trained on vast amounts of text from the internet, which contains both factual and misleading information about the world.While unintuitive from a classic view of language models, recent work has shown that the truth value of a statement can be elicited from the model's representations.This paper presents an explanation, persona hypothesis, for why LLMs appear to know the truth despite not being trained with truth labels.We hypothesize that the pretraining data i…
2024-01-01 · 2 citations
articleOpen accessSenior authorAbstraction via exemplars? A representational case study on lexical category inference in BERT
arXiv (Cornell University) · 2023-11-03 · 2 citations
preprintOpen accessSenior authorExemplar based accounts are often considered to be in direct opposition to pure linguistic abstraction in explaining language learners' ability to generalize to novel expressions. However, the recent success of neural network language models on linguistically sensitive tasks suggests that perhaps abstractions can arise via the encoding of exemplars. We provide empirical evidence for this claim by adapting an existing experiment that studies how an LM (BERT) generalizes the usage of novel tokens…
Frequent coauthors
- 43 shared
Ellie Pavlick
- 39 shared
Tal Linzen
- 36 shared
Samuel R. Bowman
- 35 shared
Benjamin Van Durme
- 32 shared
Patrick Xia
- 32 shared
Ian Tenney
- 29 shared
Alexis Ross
- 29 shared
Roma Patel
Awards & honors
- Faculty Fellow Alumni Outcomes - NYU Center for Data Science
- Moore-Sloan Fellows at CDS
- DIRAC Fellow
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