Minjoon Seo

End-to-End Question Answering Models for Goal-Oriented Dialog Learning

2019

The task of Next Utterance Classification in dialog learning highly resembles that of Question Answering, but there has not been much attention to applying models across the two fields, especially not in more practical dialog …

ClaimDiff: Comparing and Contrasting Claims on Contentious Issues

2023

With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence. However, canonical fact verification tasks do not apply to catching subtle differences in factually consistent claims, which …

How Do Large Language Models Acquire Factual Knowledge During Pretraining?

2024 · arXiv (Cornell University)

Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire factual knowledge through pretraining. This work addresses this gap …

How language models extrapolate outside the training data: A case study in Textualized Gridworld

2024 · arXiv (Cornell University)

Language models' ability to extrapolate learned behaviors to novel, more complex environments beyond their training scope is highly unknown. This study introduces a path planning task in a textualized Gridworld to probe language models' extrapolation …

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

2019

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query, which is computationally prohibitive. In this paper, we introduce query-agnostic …