Papers by Kushal Kafle
MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities (2025.acl-long)
Copied to clipboard
| Challenge: | Decoder-only large language models are increasingly being adapted for bidirectional modeling . however, their reliance on causal attention restricts their effectiveness in tasks that require understanding of bidirectional context. |
| Approach: | They propose a method to adapt decoder-only large language models to generate robust representations and infill missing text spans. |
| Outcome: | The proposed method surpasses strong decoders on token-level and sentence-level representation learning tasks and generates contextually appropriate text infills without excessive repetition of words or phrases. |
A negative case analysis of visual grounding methods for VQA (2020.acl-main)
Copied to clipboard
| Challenge: | Existing Visual Question Answering (VQA) methods exploit dataset biases and spurious statistical correlations instead of producing correct answers for the right reasons. |
| Approach: | They propose to incorporate visual cues to better ground VQA models . they also propose a regularization effect which prevents over-fitting to linguistic priors . |
| Outcome: | The proposed method outperforms existing methods on the Visual Question Answering (VQA) dataset. |