Phu Mon Htut

Training a Ranking Function for Open-Domain Question Answering

2018 · arXiv (Cornell University)

In recent years, there have been amazing advances in deep learning methods for machine reading. In machine reading, the machine reader has to extract the answer from the given ground truth paragraph. Recently, the state-of-the-art …

Towards Long Context Hallucination Detection

2025 · arXiv (Cornell University)

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. However, they are prone to contextual hallucination, generating information that is either unsubstantiated or contradictory to the given context. Although many studies have investigated …

Do Attention Heads in BERT Track Syntactic Dependencies?

2019 · arXiv (Cornell University)

We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations. We employ two methods---taking the maximum attention weight and computing the …

Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?

2020 · arXiv (Cornell University)

While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a …

Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?

2020

Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, Samuel R. Bowman. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020.