Papers by Akshen Kadakia
Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER (2022.acl-long)
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Dong-Ho Lee, Akshen Kadakia, Kangmin Tan, Mahak Agarwal, Xinyu Feng, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. |
| Approach: | They propose a demonstration-based learning method which lets the input be prefaced by task demonstrations for in-context learning. |
| Outcome: | The proposed method improves on in-domain learning and domain adaptation in low-resource settings. |
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models (2023.acl-demo)
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Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
| Challenge: | Existing models are susceptible to learning spurious biases that do not reflect the underlying task. |
| Approach: | They propose an open-source framework for explanation-based model debugging that allows users to provide various forms of feedback on model explanations. |
| Outcome: | The proposed framework improves model’s OOD performance on text classification tasks by up to 18%. |