Papers by Akshita Bhagia
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization (2022.findings-emnlp)
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| Challenge: | Combining visual modality with pretrained language models has been effective for descriptive tasks such as image captioning. |
| Approach: | They ask: do multimodal models combine visual and visual adapted language models? they find that CLIP image representations and scaling of language models do not consistently improve self-rationalization in multimodal tasks. |
| Outcome: | The proposed model types do not consistently improve self-rationalization in multimodal tasks. |
HINT: Hypernetwork Instruction Tuning for Efficient Zero- and Few-Shot Generalisation (2023.acl-long)
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| Challenge: | Recent NLP models have shown the remarkable ability to generalise ‘zero-shot’ to new tasks using only natural language instructions as guidance. |
| Approach: | They introduce Hypernetworks for INstruction Tuning (HINT) which converts task instructions and examples into parameter-efficient modules inserted into an underlying model using a pretrained text encoder. |
| Outcome: | The proposed models outperform strong state-of-the-art models by over 10% when controlling for compute. |
Continued Pretraining for Better Zero- and Few-Shot Promptability (2022.emnlp-main)
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Zhaofeng Wu, Robert L Logan IV, Pete Walsh, Akshita Bhagia, Dirk Groeneveld, Sameer Singh, Iz Beltagy
| Challenge: | Recent language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. |
| Approach: | They propose to use a dedicated pretraining stage to improve promptability in zero-shot settings and few-shot tuning. |
| Outcome: | The proposed method improves promptability in zero- and few-shot settings, while the existing method yields subpar performance. |