Papers by Duy Phung
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game (2020.findings-emnlp)
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| Challenge: | Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. |
| Approach: | They propose to use a communication game between a speaker and a listener to generate captions for PIC. |
| Outcome: | The proposed model achieves state-of-the-art performance for personal image captioning (PIC) the proposed model is based on a communication game between a speaker and a listener . |
Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters (2021.findings-acl)
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| Challenge: | Existing approaches to ED are limited due to complexity of textual data and domain shift problem. |
| Approach: | They propose to use domain adapter-based Adaptation framework to improve event detection across domains. |
| Outcome: | The proposed framework significantly boosts the performance on target domains. |
trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback (2023.emnlp-main)
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Alexander Havrilla, Maksym Zhuravinskyi, Duy Phung, Aman Tiwari, Jonathan Tow, Stella Biderman, Quentin Anthony, Louis Castricato
| Challenge: | Current RLHF paradigms rely on Proximal Policy Optimization (PPO), which quickly becomes a challenge to implement and scale up to large architectures. |
| Approach: | They propose an open-source framework for reinforcement learning from human feedback . it allows for offline fine-tuning of large language models . |
| Outcome: | The framework can be used to fine-tune models up to and exceeding 70 billion parameters. |
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)
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| Challenge: | Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR. |
| Approach: | They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |